In [10]:
import pandas as pd
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import seaborn as sns
import mlxtend
import lifetimes
In [11]:
df = pd.read_csv(r'C:\Users\HP\Desktop\market\segm\sales_data.csv')
df
Out[11]:
Order Date Order ID Product Product_ean catégorie Purchase Address Quantity Ordered Price Each Cost price turnover margin
0 2019-01-22 21:25:00 141234 iPhone 5.638009e+12 Vêtements 944 Walnut St, Boston, MA 02215 1 700.00 231.0000 700.00 469.0000
1 2019-01-28 14:15:00 141235 Lightning Charging Cable 5.563320e+12 Alimentation 185 Maple St, Portland, OR 97035 1 14.95 7.4750 14.95 7.4750
2 2019-01-17 13:33:00 141236 Wired Headphones 2.113973e+12 Vêtements 538 Adams St, San Francisco, CA 94016 2 11.99 5.9950 23.98 11.9900
3 2019-01-05 20:33:00 141237 27in FHD Monitor 3.069157e+12 Sports 738 10th St, Los Angeles, CA 90001 1 149.99 97.4935 149.99 52.4965
4 2019-01-25 11:59:00 141238 Wired Headphones 9.692681e+12 Électronique 387 10th St, Austin, TX 73301 1 11.99 5.9950 11.99 5.9950
... ... ... ... ... ... ... ... ... ... ... ...
185945 2019-12-11 20:58:00 319666 Lightning Charging Cable 6.545974e+12 Électronique 14 Madison St, San Francisco, CA 94016 1 14.95 7.4750 14.95 7.4750
185946 2019-12-01 12:01:00 319667 AA Batteries (4-pack) 5.352480e+12 Électronique 549 Willow St, Los Angeles, CA 90001 2 3.84 1.9200 7.68 3.8400
185947 2019-12-09 06:43:00 319668 Vareebadd Phone 2.674213e+12 Alimentation 273 Wilson St, Seattle, WA 98101 1 400.00 132.0000 400.00 268.0000
185948 2019-12-03 10:39:00 319669 Wired Headphones 5.216304e+12 Alimentation 778 River St, Dallas, TX 75001 1 11.99 5.9950 11.99 5.9950
185949 2019-12-21 21:45:00 319670 Bose SoundSport Headphones 8.081038e+12 Électronique 747 Chestnut St, Los Angeles, CA 90001 1 99.99 49.9950 99.99 49.9950

185950 rows × 11 columns

In [12]:
df.dtypes
Out[12]:
Order Date           object
Order ID              int64
Product              object
Product_ean         float64
catégorie            object
Purchase Address     object
Quantity Ordered      int64
Price Each          float64
Cost price          float64
turnover            float64
margin              float64
dtype: object
In [13]:
df['Order Date'] = pd.to_datetime(df['Order Date'])
In [14]:
df['month'] = df['Order Date'].dt.month
In [15]:
df['year'] = df['Order Date'].dt.year
In [16]:
df['Order Date1']=df['Order Date'].dt.to_period('M').dt.to_timestamp('M')
In [17]:
df['state']=df['Purchase Address'].apply(lambda x : x.split(',')[1])
In [18]:
le = LabelEncoder()
df['customer_id']=le.fit_transform(df[['Purchase Address']])

C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\preprocessing\_label.py:116: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
In [22]:
df.drop(columns = 'Purchase Address',inplace=True)
In [23]:
df
Out[23]:
Order Date Order ID Product Product_ean catégorie Quantity Ordered Price Each Cost price turnover margin month year Order Date1 state customer_id
0 2019-01-22 21:25:00 141234 iPhone 5.638009e+12 Vêtements 1 700.00 231.0000 700.00 469.0000 1 2019 2019-01-31 Boston 132266
1 2019-01-28 14:15:00 141235 Lightning Charging Cable 5.563320e+12 Alimentation 1 14.95 7.4750 14.95 7.4750 1 2019 2019-01-31 Portland 13519
2 2019-01-17 13:33:00 141236 Wired Headphones 2.113973e+12 Vêtements 2 11.99 5.9950 23.98 11.9900 1 2019 2019-01-31 San Francisco 68669
3 2019-01-05 20:33:00 141237 27in FHD Monitor 3.069157e+12 Sports 1 149.99 97.4935 149.99 52.4965 1 2019 2019-01-31 Los Angeles 99824
4 2019-01-25 11:59:00 141238 Wired Headphones 9.692681e+12 Électronique 1 11.99 5.9950 11.99 5.9950 1 2019 2019-01-31 Austin 44894
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
185945 2019-12-11 20:58:00 319666 Lightning Charging Cable 6.545974e+12 Électronique 1 14.95 7.4750 14.95 7.4750 12 2019 2019-12-31 San Francisco 6487
185946 2019-12-01 12:01:00 319667 AA Batteries (4-pack) 5.352480e+12 Électronique 2 3.84 1.9200 7.68 3.8400 12 2019 2019-12-31 Los Angeles 70444
185947 2019-12-09 06:43:00 319668 Vareebadd Phone 2.674213e+12 Alimentation 1 400.00 132.0000 400.00 268.0000 12 2019 2019-12-31 Seattle 27410
185948 2019-12-03 10:39:00 319669 Wired Headphones 5.216304e+12 Alimentation 1 11.99 5.9950 11.99 5.9950 12 2019 2019-12-31 Dallas 106162
185949 2019-12-21 21:45:00 319670 Bose SoundSport Headphones 8.081038e+12 Électronique 1 99.99 49.9950 99.99 49.9950 12 2019 2019-12-31 Los Angeles 101282

185950 rows × 15 columns

Customers¶

Cohort Analysis¶

In [30]:
df['Min_date']=df.groupby("customer_id")["Order Date"].transform('min')
In [39]:
df['Min_date']=df['Min_date'].dt.to_period('M').dt.to_timestamp('M')
In [49]:
df['diff']=(df['Order Date1']-df['Min_date'])
In [60]:
df['diff']=(df['diff'].dt.days/30).astype(int)+1
In [73]:
cohort_table = df.pivot_table(index = 'Min_date' ,columns = 'diff' ,values='Order ID' ,aggfunc='count')
cohort_table = cohort_table.div(cohort_table[1],axis=0)
In [91]:
plt.subplots(figsize=(14,8))
sns.heatmap(cohort_table ,cmap='Blues' ,annot=True,fmt='.5g')
plt.show()
In [92]:
cohort_table1 = df.pivot_table(index = 'Min_date' ,columns = 'diff' ,values='Price Each' ,aggfunc='sum')
cohort_table1 = cohort_table1.div(cohort_table1[1],axis=0)
In [93]:
plt.subplots(figsize=(18,8))
sns.heatmap(cohort_table1 ,cmap='Blues' ,annot=True,fmt='.8g')
plt.show()
  • We cannot keep customers for long !!
In [100]:
df['state']=df['state'].str.strip()
df['state'].unique()
Out[100]:
array(['Boston', 'Portland', 'San Francisco', 'Los Angeles', 'Austin',
       'Atlanta', 'Seattle', 'New York City', 'Dallas'], dtype=object)
  • dive deep into cohort table to gain more insights
In [112]:
for _ in df['state'].unique():
    cohort_table = df.query(f"state == '{_}'").pivot_table(index = 'Min_date' ,columns = 'diff' ,values='Order ID' ,aggfunc='count')
    cohort_table = cohort_table.div(cohort_table[1],axis=0)
    plt.subplots(figsize=(14,8))
    sns.heatmap(cohort_table ,cmap='Blues' ,annot=True,fmt='.5g')
    plt.show()
  • all states have the same thing ( We cannot keep customers for long !!)

CLTV¶

In [198]:
from lifetimes.utils import summary_data_from_transaction_data
from lifetimes import GammaGammaFitter,ModifiedBetaGeoFitter,BetaGeoBetaBinomFitter
In [122]:
T = summary_data_from_transaction_data(transactions=df ,customer_id_col='customer_id' ,datetime_col='Order Date' ,monetary_value_col='Price Each')
In [127]:
T = T.query("monetary_value > 0")
In [192]:
gamma = GammaGammaFitter(penalizer_coef=0.00001)
In [200]:
T.corr()
Out[200]:
frequency recency T monetary_value
frequency 1.000000 0.301791 0.155754 0.011661
recency 0.301791 1.000000 0.565643 0.009438
T 0.155754 0.565643 1.000000 0.009844
monetary_value 0.011661 0.009438 0.009844 1.000000
In [131]:
gamma.fit(T['frequency'] ,T['monetary_value'])
Out[131]:
<lifetimes.GammaGammaFitter: fitted with 30724 subjects, p: 2.49, q: 0.44, v: 2.04>
In [203]:
mbg = ModifiedBetaGeoFitter(penalizer_coef=0.00001)
In [204]:
mbg.fit(T['frequency'],T['recency'],T['T'])

C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\arraylike.py:397: RuntimeWarning: invalid value encountered in sqrt
  result = getattr(ufunc, method)(*inputs, **kwargs)
Out[204]:
<lifetimes.ModifiedBetaGeoFitter: fitted with 30724 subjects, a: 0.00, alpha: 4839.27, b: 0.00, r: 25.24>
In [205]:
from lifetimes.plotting import plot_probability_alive_matrix
In [206]:
from lifetimes.plotting import plot_frequency_recency_matrix
In [207]:
from lifetimes.plotting import plot_expected_repeat_purchases
In [208]:
plt.subplots(figsize=(14,8))
plot_probability_alive_matrix(model=mbg)
plt.show()
  • all customers have high probability about 0.99 that they will be alive
  • (The ModifiedBetaGeoFitter model predicted that customers who stopped purchasing or continued purchasing would change their behavior in the future) and this is the explanation of this results
In [216]:
plt.subplots( figsize=(14, 8))
plot_frequency_recency_matrix(model=mbg, T=30 )
plt.show()
  • customers who's frequency more than 5 have highest probability about 0.18 that will buy next 30 days
In [210]:
plt.subplots(figsize=(14, 8))
plot_expected_repeat_purchases(model=mbg)
plt.show()

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
  • old customers ( more than 1 year ) have a chance to buy multiple times than new customers
In [224]:
gamma.fit(frequency=T['frequency'] ,monetary_value=T['monetary_value'])
Out[224]:
<lifetimes.GammaGammaFitter: fitted with 30724 subjects, p: 11.35, q: 0.50, v: 0.56>
In [231]:
cltv = gamma.customer_lifetime_value(transaction_prediction_model=mbg ,frequency=T['frequency'] ,recency=T['recency'] ,T = T['T'] ,monetary_value = T['monetary_value'] ,time=1 ,discount_rate=0)
In [232]:
cltv
Out[232]:
customer_id
5         285.824104
7           2.499445
18          1.990330
22          2.049728
25          0.710052
             ...    
140750     17.972122
140757      0.565830
140768     96.132788
140769      2.072777
140781      2.007943
Name: clv, Length: 30724, dtype: float64
In [236]:
states = df.groupby("customer_id")["state"].unique().apply(lambda x : x[0])
In [242]:
states = states.to_frame()
In [243]:
cltv = cltv.to_frame()
In [246]:
final_cltv = states.merge(cltv ,how = 'left',right_index=True ,left_index=True)
In [247]:
final_cltv = final_cltv.fillna(0)
In [258]:
final_cltv['clv']=final_cltv['clv']/final_cltv['clv'].max()
In [265]:
def groups(x):
    if x <= 0.00508:
        return 'weak'
    elif x > 0.00508 and x <= 0.0601:
        return 'medium'
    else :
        return 'strong'
In [269]:
final_cltv['clv_gro'] = final_cltv['clv'].apply(groups)
In [274]:
final_cltv.query("clv > 0")
Out[274]:
state clv clv_gro
customer_id
5 San Francisco 0.708835 strong
7 New York City 0.006199 medium
18 Los Angeles 0.004936 weak
22 San Francisco 0.005083 medium
25 Los Angeles 0.001761 weak
... ... ... ...
140750 Los Angeles 0.044570 medium
140757 Los Angeles 0.001403 weak
140768 Los Angeles 0.238406 strong
140769 San Francisco 0.005140 medium
140781 Seattle 0.004980 weak

30724 rows × 3 columns

In [276]:
plt.subplots(figsize=(14,8))
sns.countplot(data=final_cltv ,x= 'clv_gro')
sns.despine()
plt.show()
In [277]:
plt.subplots(figsize=(14,8))
sns.countplot(data=final_cltv ,x='state' ,hue = 'clv_gro')
sns.despine()
plt.show()
  • based on the last two visualization ,most of our customers are weak ( have low CLTV for one month from now ) !!!
  • While los Angeles & San Francisco and New York have highest numbers of weak customers but also have highest number of strong and medium customers

States¶

In [279]:
import numpy as np
In [322]:
plt.subplots(figsize=(14,8))
sns.barplot(data = df ,x='state' ,y='Price Each' ,estimator=np.sum ,order=['Austin', 'Portland', 'Seattle', 'Dallas', 'Atlanta', 'Boston',
       'New York City', 'Los Angeles', 'San Francisco'])
sns.lineplot(data=df.groupby(["state"])['Quantity Ordered'].sum().sort_values().reset_index() ,x='state' ,y='Quantity Ordered' ,marker='s', color='red')
sns.despine()
plt.yscale('log')
plt.show()
  • New York City, Los Angeles, San Francisco have the highest revenue also highest n-quantities
In [292]:
x = df.pivot_table(index = 'state' ,columns = 'Order Date1' ,values = 'Price Each' ,aggfunc='sum' ,margins=True)
x
Out[292]:
Order Date1 2019-01-31 00:00:00 2019-02-28 00:00:00 2019-03-31 00:00:00 2019-04-30 00:00:00 2019-05-31 00:00:00 2019-06-30 00:00:00 2019-07-31 00:00:00 2019-08-31 00:00:00 2019-09-30 00:00:00 2019-10-31 00:00:00 2019-11-30 00:00:00 2019-12-31 00:00:00 2020-01-31 00:00:00 All
state
Atlanta 148523.69 175851.81 230134.86 282870.88 237572.66 218858.60 210979.19 168443.29 169923.85 304508.46 273143.85 359097.06 NaN 2779908.20
Austin 87163.21 108325.58 153671.83 171487.65 159802.17 143193.06 149775.88 125146.00 105825.92 202100.25 170441.96 232440.12 499.98 1809873.61
Boston 198912.69 213629.24 298970.37 351669.70 325999.80 252741.72 290151.72 236932.64 246591.73 364265.09 349623.53 506594.65 1326.89 3637409.77
Dallas 141158.53 185438.29 221205.40 250209.05 267131.07 185841.21 211201.99 178958.29 163076.03 321353.23 247127.45 378212.33 1714.95 2752627.82
Los Angeles 285754.49 339637.47 427827.95 546855.97 497151.19 449351.78 392183.65 344302.64 352525.82 609228.15 496374.57 679241.56 999.99 5421435.23
New York City 256608.67 303046.15 365148.88 445252.65 433439.97 322462.56 353338.28 301152.88 298981.09 485336.03 425707.06 643612.89 1283.72 4635370.83
Portland 114310.51 148571.15 185649.95 238942.93 230922.52 168857.59 175397.95 151878.13 131816.96 252024.64 206659.16 302562.99 152.99 2307747.47
San Francisco 431292.63 544218.19 690363.85 805693.57 772384.31 607694.75 638095.00 535065.50 460938.06 861937.47 760902.41 1100501.11 2374.89 8211461.74
Seattle 139540.56 170166.84 218234.74 274688.62 210721.44 213024.34 211415.90 188466.05 155312.63 314801.51 250620.69 386152.70 149.99 2733296.01
All 1803264.98 2188884.72 2791207.83 3367671.02 3135125.13 2562025.61 2632539.56 2230345.42 2084992.09 3715554.83 3180600.68 4588415.41 8503.40 34289130.68
In [299]:
x = x.div(x.iloc[-1,:])*100
In [303]:
x = round(x,2)
In [304]:
plt.subplots(figsize=(18,8))
sns.heatmap(x ,annot=True ,cmap='Blues',fmt='.8g')
plt.show()
  • based on the previous visualization New York City, Los Angeles, San Francisco have the highest revenue for every month in a row
  • while these states have highest n-weak customers but also have the highest revenue for every month in row !!
  • also these states have highes n-strong and medium customers
In [306]:
df
Out[306]:
Order Date Order ID Product Product_ean catégorie Quantity Ordered Price Each Cost price turnover margin month year Order Date1 state customer_id Min_date diff
0 2019-01-22 21:25:00 141234 iPhone 5.638009e+12 Vêtements 1 700.00 231.0000 700.00 469.0000 1 2019 2019-01-31 Boston 132266 2019-01-31 1
1 2019-01-28 14:15:00 141235 Lightning Charging Cable 5.563320e+12 Alimentation 1 14.95 7.4750 14.95 7.4750 1 2019 2019-01-31 Portland 13519 2019-01-31 1
2 2019-01-17 13:33:00 141236 Wired Headphones 2.113973e+12 Vêtements 2 11.99 5.9950 23.98 11.9900 1 2019 2019-01-31 San Francisco 68669 2019-01-31 1
3 2019-01-05 20:33:00 141237 27in FHD Monitor 3.069157e+12 Sports 1 149.99 97.4935 149.99 52.4965 1 2019 2019-01-31 Los Angeles 99824 2019-01-31 1
4 2019-01-25 11:59:00 141238 Wired Headphones 9.692681e+12 Électronique 1 11.99 5.9950 11.99 5.9950 1 2019 2019-01-31 Austin 44894 2019-01-31 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
185945 2019-12-11 20:58:00 319666 Lightning Charging Cable 6.545974e+12 Électronique 1 14.95 7.4750 14.95 7.4750 12 2019 2019-12-31 San Francisco 6487 2019-12-31 1
185946 2019-12-01 12:01:00 319667 AA Batteries (4-pack) 5.352480e+12 Électronique 2 3.84 1.9200 7.68 3.8400 12 2019 2019-12-31 Los Angeles 70444 2019-05-31 8
185947 2019-12-09 06:43:00 319668 Vareebadd Phone 2.674213e+12 Alimentation 1 400.00 132.0000 400.00 268.0000 12 2019 2019-12-31 Seattle 27410 2019-12-31 1
185948 2019-12-03 10:39:00 319669 Wired Headphones 5.216304e+12 Alimentation 1 11.99 5.9950 11.99 5.9950 12 2019 2019-12-31 Dallas 106162 2019-12-31 1
185949 2019-12-21 21:45:00 319670 Bose SoundSport Headphones 8.081038e+12 Électronique 1 99.99 49.9950 99.99 49.9950 12 2019 2019-12-31 Los Angeles 101282 2019-12-31 1

185950 rows × 17 columns

My Insights¶

  • The company cannot retain customers ,there are so many reasons to can't retain customers maybe the products or serives or marketing methods and so on ,i have no available data to say which one is the reason
  • big portion of our customers are weak customers ,all states have weak customers with almost the same number ,but New York City, Los Angeles, San Francisco have highest portion of medium and strong customers which make them very important states and we can' focus our attention these states
  • also these states have highest percentage of revenue over all months
  • if i have more information about the customers i can give insights about which category of customers to target to gain more strong and medium customers
  • all customers have a 0.19 to buy from us next month
  • old customers ( more than 1 year ) will buy multiple times

Product¶

ABC Analysis¶

In [363]:
abc = df.groupby("Product")["Price Each"].sum().sort_values(ascending=False).reset_index()
In [367]:
abc['Price Each'].sum()
Out[367]:
34289130.68000001
In [370]:
abc['portion']=abc['Price Each'] / abc['Price Each'].sum()
In [374]:
abc['cumsum']=abc['portion'].cumsum()
In [377]:
def seg(x):
    if x <= 0.8:
        return 'x'
    elif x >0.8 and x <=0.9:
        return 'y'
    else :
        return 'z'
In [379]:
abc['group']=abc['cumsum'].apply(seg)
In [382]:
plt.subplots(figsize=(14,8))
sns.barplot(data=abc ,x='Product' ,y='portion' ,hue='group')
sns.despine()
plt.xticks(rotation=90)
plt.show()
In [387]:
abc_months = df.groupby(["Order Date1","Product"])["Price Each"].sum().reset_index()
In [392]:
abc_months['total']=abc_months.groupby("Order Date1")['Price Each'].transform('sum')
In [396]:
abc_months['portaion']=abc_months['Price Each'] /abc_months['total']
In [399]:
abc_months = abc_months.sort_values(by=['Order Date1','portaion'],ascending=[True ,False])
In [405]:
abc_months['cumsum']=abc_months.groupby("Order Date1")["portaion"].cumsum()
In [409]:
abc_months['group']=abc_months['cumsum'].apply(seg)
In [422]:
plt.subplots(figsize=(14,8))
sns.heatmap(abc_months[['Order Date1','Product','group','cumsum']].query("group == 'x'").pivot_table(index = ['Order Date1','group'],columns='Product' ,aggfunc='count'),cmap='Blues' ,annot=True)
plt.show()
  • based on last two visualization ( Iphone ,Thinkpad Laptop ,Macbook Pro Labtop , Google Iphone ,27 in 4k gaming monitor ,apple airpoids headpohones and 34in ultrwide monitor get the big portion of profit every month and overall
In [446]:
target=df[df['Product'].isin(['27in 4K Gaming Monitor', '27in FHD Monitor', '34in Ultrawide Monitor',
       'Apple Airpods Headphones', 'Google Phone', 'Macbook Pro Laptop',
       'ThinkPad Laptop', 'iPhone'])]
In [447]:
target=target.groupby(["state","Product"])['Price Each'].sum().reset_index().pivot_table(index='state' ,columns='Product' ,values='Price Each' ,aggfunc='sum',margins=True)
In [451]:
target=target.div(target.iloc[-1])
In [454]:
target=round(target,2)
In [455]:
target
Out[455]:
Product 27in 4K Gaming Monitor 27in FHD Monitor 34in Ultrawide Monitor Apple Airpods Headphones Google Phone Macbook Pro Laptop ThinkPad Laptop iPhone All
state
Atlanta 0.08 0.08 0.08 0.08 0.08 0.08 0.09 0.08 0.08
Austin 0.05 0.05 0.05 0.06 0.05 0.05 0.05 0.06 0.05
Boston 0.11 0.11 0.11 0.11 0.11 0.10 0.11 0.11 0.11
Dallas 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08
Los Angeles 0.16 0.16 0.15 0.16 0.15 0.16 0.16 0.16 0.16
New York City 0.13 0.14 0.14 0.13 0.14 0.14 0.14 0.13 0.14
Portland 0.07 0.07 0.07 0.07 0.06 0.07 0.07 0.07 0.07
San Francisco 0.23 0.24 0.23 0.24 0.25 0.24 0.23 0.24 0.24
Seattle 0.09 0.08 0.09 0.08 0.08 0.08 0.08 0.08 0.08
All 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
  • based on the previous table if we can San Francisco & New York City & Los Angeles & Boston sold the highest portion of revenue from 27in 4K Gaming Monitor 27in FHD Monitor 34in Ultrawide Monitor Apple Airpods Headphones Google Phone Macbook Pro Laptop ThinkPad Laptop iPhone and these products are the most profitable products as we saw on the previous visualizations
  • we can choose these areas to sell similar products rather than other areas

Data Mining (Association Rule)¶

In [505]:
def dm(x):
    if x >=1:
        return 1
    else :
        return 0
In [503]:
basket = df.groupby(["Order ID","Product"]).apply(len).unstack()
In [504]:
basket.fillna(0,inplace=True)
In [508]:
basket = basket.applymap(dm)
In [509]:
basket.describe()
Out[509]:
Product 20in Monitor 27in 4K Gaming Monitor 27in FHD Monitor 34in Ultrawide Monitor AA Batteries (4-pack) AAA Batteries (4-pack) Apple Airpods Headphones Bose SoundSport Headphones Flatscreen TV Google Phone LG Dryer LG Washing Machine Lightning Charging Cable Macbook Pro Laptop ThinkPad Laptop USB-C Charging Cable Vareebadd Phone Wired Headphones iPhone
count 178437.000000 178437.000000 178437.000000 178437.000000 178437.000000 178437.000000 178437.000000 178437.000000 178437.000000 178437.000000 178437.00000 178437.000000 178437.000000 178437.000000 178437.000000 178437.000000 178437.000000 178437.000000 178437.000000
mean 0.022966 0.034886 0.042020 0.034600 0.115122 0.115408 0.087005 0.074525 0.026867 0.030946 0.00362 0.003732 0.121074 0.026458 0.023123 0.122480 0.011573 0.105623 0.038333
std 0.149796 0.183492 0.200636 0.182766 0.319170 0.319514 0.281844 0.262624 0.161694 0.173173 0.06006 0.060980 0.326214 0.160492 0.150295 0.327841 0.106953 0.307355 0.191999
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
75% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
max 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
In [510]:
basket['total'] = basket.sum(axis=1)
In [511]:
basket = basket.query("total > 1")
In [512]:
basket.drop(columns = 'total',inplace=True)
In [513]:
basket = basket.replace([0,1],[False ,True])
In [514]:
basket
Out[514]:
Product 20in Monitor 27in 4K Gaming Monitor 27in FHD Monitor 34in Ultrawide Monitor AA Batteries (4-pack) AAA Batteries (4-pack) Apple Airpods Headphones Bose SoundSport Headphones Flatscreen TV Google Phone LG Dryer LG Washing Machine Lightning Charging Cable Macbook Pro Laptop ThinkPad Laptop USB-C Charging Cable Vareebadd Phone Wired Headphones iPhone
Order ID
141275 False False False False False False False False False False False False False False False True False True False
141290 False False False False True False True False False False False False False False False False False False False
141365 False False False False False False False False False False False False False False False False True True False
141384 False False False False False False False False False True False False False False False True False False False
141450 False False False False False False False True False True False False False False False False False False False
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
319536 False False False False False False False False False False False False False True False False False True False
319556 False False False False False False False False False True False False False False False False False True False
319584 False False False False False False False False False False False False False False False False False True True
319596 False False False False False False False False False False False False True False False False False False True
319631 False False False True False False False False False False False False True False False False False False False

6832 rows × 19 columns

In [538]:
from mlxtend.frequent_patterns import fpgrowth ,association_rules
In [539]:
model = fpgrowth(df = basket ,min_support=0.05 ,use_colnames=True,verbose=10)

10 itemset(s) from tree conditioned on items ()
0 itemset(s) from tree conditioned on items (USB-C Charging Cable)
2 itemset(s) from tree conditioned on items (Wired Headphones)
0 itemset(s) from tree conditioned on items (Wired Headphones, iPhone)
0 itemset(s) from tree conditioned on items (Wired Headphones, Google Phone)
1 itemset(s) from tree conditioned on items (Apple Airpods Headphones)
0 itemset(s) from tree conditioned on items (AA Batteries (4-pack))
1 itemset(s) from tree conditioned on items (Vareebadd Phone)
1 itemset(s) from tree conditioned on items (Google Phone)
0 itemset(s) from tree conditioned on items (Bose SoundSport Headphones)
0 itemset(s) from tree conditioned on items (iPhone)
1 itemset(s) from tree conditioned on items (Lightning Charging Cable)
0 itemset(s) from tree conditioned on items (AAA Batteries (4-pack))
In [540]:
model
Out[540]:
support itemsets
0 0.295228 (USB-C Charging Cable)
1 0.234924 (Wired Headphones)
2 0.135539 (Apple Airpods Headphones)
3 0.102166 (AA Batteries (4-pack))
4 0.087968 (Vareebadd Phone)
5 0.239022 (Google Phone)
6 0.112119 (Bose SoundSport Headphones)
7 0.272834 (iPhone)
8 0.252049 (Lightning Charging Cable)
9 0.105240 (AAA Batteries (4-pack))
10 0.067623 (iPhone, Wired Headphones)
11 0.061768 (Wired Headphones, Google Phone)
12 0.054596 (iPhone, Apple Airpods Headphones)
13 0.053864 (USB-C Charging Cable, Vareebadd Phone)
14 0.145931 (USB-C Charging Cable, Google Phone)
15 0.147980 (iPhone, Lightning Charging Cable)
In [541]:
import MarketBaksetNetwork
In [542]:
from MarketBaksetNetwork import draw_network
In [546]:
relations = association_rules(df = model ,metric='lift' ,min_threshold=1)
In [549]:
relations = relations.query("confidence > 0.5")
In [550]:
relations
Out[550]:
antecedents consequents antecedent support consequent support support confidence lift leverage conviction zhangs_metric
7 (Vareebadd Phone) (USB-C Charging Cable) 0.087968 0.295228 0.053864 0.612313 2.074031 0.027893 1.817887 0.567795
9 (Google Phone) (USB-C Charging Cable) 0.239022 0.295228 0.145931 0.610533 2.068002 0.075365 1.809579 0.678655
10 (iPhone) (Lightning Charging Cable) 0.272834 0.252049 0.147980 0.542382 2.151889 0.079213 1.634444 0.736134
11 (Lightning Charging Cable) (iPhone) 0.252049 0.272834 0.147980 0.587108 2.151889 0.079213 1.761154 0.715678
In [560]:
plt.subplots(figsize=(14,8))
draw_network(relations ,rules_to_show=3)
  • Relation 1 : people who buy Vareebadd Phone are more likely to buy USB-C Charging Cable
  • Relation 2 : people who buy Google Phone are more likely to buy USB-C Charging Cable
  • Relation 3 : people who buy iPhone are more likely to buy Lightning Charging Cable
  • we can cross sell
  • iPhone with Lightning Charging Cable
  • Vareebadd Phone ,Google Phone with USB-C Charging Cable
In [565]:
df['Order Date2']=df['Order Date'].dt.to_period('D')

Forecast next 7 days sells¶

In [567]:
time_series = df.groupby("Order Date2")["Price Each"].sum()
In [572]:
plt.subplots(figsize=(14,8))
time_series.plot()
sns.despine()
plt.show()
In [575]:
import RootsTimeSeries
from RootsTimeSeries import CharacteristicPlotter
from scipy.stats import shapiro
from statsmodels.stats.stattools import durbin_watson
from statsmodels.stats.diagnostic import acorr_ljungbox
from statsmodels.stats.diagnostic import het_white
from statsmodels.graphics.gofplots import qqplot
import statsmodels
import pmdarima
import skforecast
import sklearn
from statsmodels.tsa.stattools import adfuller
from statsmodels.graphics.tsaplots import plot_acf,plot_pacf
from statsmodels.tsa.arima.model import ARIMA
from pmdarima import auto_arima
from skforecast.ForecasterAutoreg import ForecasterAutoreg
from skforecast.ForecasterAutoregDirect import ForecasterAutoregDirect
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
In [576]:
if adfuller(time_series)[1] <= 0.05:
    print(f"stationray:{adfuller(time_series)[1]}")
else :
    print("non stationray")

non stationray
In [578]:
if adfuller(time_series.diff().dropna())[1] <= 0.05:
    print(f"stationray:{adfuller(time_series.diff().dropna())[1]}")
else :
    print("non stationray")

stationray:7.142264907245654e-23
In [579]:
ax,(ax ,ax1) = plt.subplots(nrows=1,ncols=2,figsize=(14,8))
plot_pacf(time_series.diff().dropna(),ax=ax)
plot_acf(time_series.diff().dropna(),ax=ax1)
sns.despine()

AR(1) ,MA(1)

In [603]:
time_series.index=time_series.index.to_timestamp()
In [604]:
# split train test cv
_ ,X_test = train_test_split(time_series ,train_size=0.8 ,test_size=0.2 ,shuffle=False)
X_train ,X_cv = train_test_split(_ ,train_size=0.8 ,test_size=0.2 ,shuffle=False)
In [605]:
# fit the mode
model = ARIMA(endog=X_train ,order=(1,0,1)).fit()
In [606]:
plt.subplots(figsize=(14,8))
X_train.plot(label='train')
model.predict().plot(label='forecast')
sns.despine()
plt.show()
In [607]:
# train score
train_score = r2_score(X_train ,model.predict())
train_score
Out[607]:
0.7123195311128949
In [608]:
X_train1 = X_train.copy()
X_train2 = X_train.copy()
In [609]:
# cv score Walk Forward Method
predictions = []
for _ in range(len(X_cv)):
    X_train1 = X_train1.append(X_cv.iloc[_:_+1])
    model1 = ARIMA(endog=X_train1 ,order=(1,0,1)).fit()
    predictions.append(model1.forecast().values[0])

C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
In [610]:
# test score Walk Forward Method
predictions1 = []
for _ in range(len(X_test)):
    X_train2 = X_train2.append(X_test.iloc[_:_+1])
    model2 = ARIMA(endog=X_train2 ,order=(1,0,1)).fit()
    predictions1.append(model2.forecast().values[0])

C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
In [611]:
train_score,r2_score(X_cv ,predictions),r2_score(X_test ,predictions1)
Out[611]:
(0.7123195311128949, 0.8601769713874752, 0.681992099389361)
  • wow the model do better on unseen data on CV data and Test closes to train !!
In [614]:
fig, (ax, ax1 ,ax2) = plt.subplots(nrows=1, ncols=3, figsize=(14, 8))

# Plotting on the first subplot
sns.lineplot(x=X_test.index, y=X_test.values, ax=ax2, label='test')
sns.lineplot(x=X_test.index, y=predictions1, ax=ax2, label='Predicted')

# Plotting on the second subplot
sns.lineplot(x=X_cv.index, y=X_cv.values, ax=ax1, label='cv')
sns.lineplot(x=X_cv.index, y=predictions, ax=ax1, label='Predicted')

# Plotting on the second subplot
sns.lineplot(x=X_train.index, y=X_train.values, ax=ax, label='train')
sns.lineplot(x=X_train.index, y=model.predict().values, ax=ax, label='Predicted')

# Adding legends
ax1.legend()
ax2.legend()
ax.legend()
sns.despine()
ax.tick_params(axis='x', rotation=90)
ax1.tick_params(axis='x', rotation=90)
ax2.tick_params(axis='x', rotation=90)
# Display the plots
plt.show()
In [615]:
model_auto = auto_arima(y = X_train ,start_p=1 ,start_q= 1 ,max_p= 3 ,max_q=3 ,seasonal=False)
In [623]:
model_auto
Out[623]:
 ARIMA(0,1,1)(0,0,0)[0]
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
 ARIMA(0,1,1)(0,0,0)[0]
In [617]:
# new model
# fit the mode
X_train3 = X_train.copy()
X_train4 = X_train.copy()
new_model = ARIMA(endog=X_train ,order=(0,1,1)).fit()
# cv score Walk Forward Method
predictions2 = []
for _ in range(len(X_cv)):
    X_train3 = X_train3.append(X_cv.iloc[_:_+1])
    new_model1 = ARIMA(endog=X_train3 ,order=(0,1,1)).fit()
    predictions2.append(new_model1.forecast().values[0])
# test score Walk Forward Method
predictions3 = []
for _ in range(len(X_test)):
    X_train4 = X_train4.append(X_test.iloc[_:_+1])
    new_model2 = ARIMA(endog=X_train4 ,order=(0,1,1)).fit()
    predictions3.append(new_model2.forecast().values[0])

C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
In [618]:
r2_score(X_train ,new_model.predict()),r2_score(X_cv ,predictions2),r2_score(X_test ,predictions3)
Out[618]:
(0.6461331426790304, 0.8634950082876237, 0.6777327275799437)

this model do better on generalization than first model

In [619]:
fig, (ax, ax1 ,ax2) = plt.subplots(nrows=1, ncols=3, figsize=(14, 8))

# Plotting on the first subplot
sns.lineplot(x=X_test.index, y=X_test.values, ax=ax2, label='test')
sns.lineplot(x=X_test.index, y=predictions3, ax=ax2, label='Predicted')

# Plotting on the second subplot
sns.lineplot(x=X_cv.index, y=X_cv.values, ax=ax1, label='cv')
sns.lineplot(x=X_cv.index, y=predictions2, ax=ax1, label='Predicted')

# Plotting on the second subplot
sns.lineplot(x=X_train.index, y=X_train.values, ax=ax, label='train')
sns.lineplot(x=X_train.index, y=new_model.predict().values, ax=ax, label='Predicted')

# Adding legends
ax1.legend()
ax2.legend()
ax.legend()
sns.despine()
ax.tick_params(axis='x', rotation=90)
ax1.tick_params(axis='x', rotation=90)
ax2.tick_params(axis='x', rotation=90)
# Display the plots
plt.show()
In [620]:
fig, (ax, ax1 ,ax2) = plt.subplots(nrows=1, ncols=3, figsize=(14, 8))

# Plotting on the first subplot
sns.lineplot(x=X_test.index, y=X_test.values, ax=ax2, label='test')
sns.lineplot(x=X_test.index, y=predictions1, ax=ax2, label='Predicted')

# Plotting on the second subplot
sns.lineplot(x=X_cv.index, y=X_cv.values, ax=ax1, label='cv')
sns.lineplot(x=X_cv.index, y=predictions, ax=ax1, label='Predicted')

# Plotting on the second subplot
sns.lineplot(x=X_train.index, y=X_train.values, ax=ax, label='train')
sns.lineplot(x=X_train.index, y=model.predict().values, ax=ax, label='Predicted')

# Adding legends
ax1.legend()
ax2.legend()
ax.legend()
sns.despine()
ax.tick_params(axis='x', rotation=90)
ax1.tick_params(axis='x', rotation=90)
ax2.tick_params(axis='x', rotation=90)
# Display the plots
plt.show()
In [624]:
import RootsTimeSeries
from RootsTimeSeries import CharacteristicPlotter
from scipy.stats import shapiro
from statsmodels.stats.stattools import durbin_watson
from statsmodels.stats.diagnostic import acorr_ljungbox
from statsmodels.stats.diagnostic import het_white ,het_arch
from statsmodels.graphics.gofplots import qqplot
In [626]:
model.params[2]
Out[626]:
(0.9918325978063867, -0.7525016041380406)
In [629]:
params = [[model.params[1]],[model.params[2]]]
In [630]:
checks1 = CharacteristicPlotter(coef=params ,ar_ma='ar_ma')

stationary : True 
invertible : True
In [631]:
checks1.plot_roots()
In [632]:
shapiro(model.resid)[1],shapiro(new_model.resid)[1]
Out[632]:
(0.4571443796157837, 4.6702254508090846e-08)
In [634]:
fig,(ax,ax1)=plt.subplots(nrows=2,ncols=2,figsize=(14,8))
sns.histplot(model.resid,ax=ax[0] ,label='model')
sns.histplot(new_model.resid,ax=ax1[0],label='model2')
qqplot(model.resid,ax=ax[1] ,label='model',line='s')
qqplot(new_model1.resid,ax=ax1[1],label='model2',line='s')
sns.despine()
ax[0].legend()
ax1[0].legend()
plt.show()
In [635]:
if durbin_watson(model.resid) <= 2.1 and durbin_watson(model.resid) >= 1.9 :
    print('no first order autocorrelation')
else:
    print('first order autocorrelation')

no first order autocorrelation
In [636]:
if durbin_watson(new_model.resid) <= 2.1 and durbin_watson(new_model.resid) >= 1.9 :
    print('no first order autocorrelation')
else:
    print('first order autocorrelation')

first order autocorrelation
In [639]:
acorr_ljungbox(model.resid,lags=5)['lb_pvalue'].values,acorr_ljungbox(new_model.resid,lags=5)['lb_pvalue'].values
Out[639]:
(array([0.6642226 , 0.85085964, 0.70492698, 0.84373753, 0.92402212]),
 array([0.3414813 , 0.55313309, 0.42846029, 0.57551609, 0.71349413]))
  • no high order autocorrelation till 5 lags
  • first model is better than second model
In [646]:
# forecast 7 points using recursive method
new_model_final = ARIMA(endog=time_series ,order=(0,1,1)).fit()
time_series.append(new_model_final.forecast(steps=7))
Out[646]:
2019-01-01    65420.910000
2019-01-02    70407.840000
2019-01-03    46793.740000
2019-01-04    61779.510000
2019-01-05    46160.620000
                  ...     
2020-01-04    92151.573015
2020-01-05    92151.573015
2020-01-06    92151.573015
2020-01-07    92151.573015
2020-01-08    92151.573015
Freq: D, Length: 373, dtype: float64
In [ ]:
In [649]:
plt.subplots(figsize=(14,8))
plt.plot(time_series[-7:])
plt.plot(time_series.append(new_model_final.forecast(steps=7))[-14:],'--')
sns.despine()
plt.title("Forecasting next 4 weeks")
plt.show()
  • the forecast line die because recursive method based on predictions ,predictions have some error term and when forecasting recursively we gain more error on the previous error of the previous point so this method isn't good

Direct Method¶

In [654]:
for _ in range(1,150,1):
    mod = ForecasterAutoregDirect(regressor=LinearRegression() ,steps = 7 ,lags=_)
    mod.fit(X_train)
    print(r2_score(X_cv.iloc[:7].values,mod.predict().values),_)

-2.151254054856309 1
-0.42260733390226757 2
-0.13729987215929462 3
-0.31226929475993925 4
-0.2507792317684303 5
-0.25702278775503307 6
-0.1566357883821632 7
-0.14354521125239939 8
-0.08719138136947446 9
-0.0628755302165851 10
-0.007681173672709152 11
-0.40452355662884343 12
-0.3732350878556232 13
-0.42544533931136597 14
-0.42771074908205753 15
-0.500343355197421 16
-0.512845170459588 17
-0.424909540339804 18
-0.3755096542251595 19
-0.4598393971152801 20
-0.45581065099983564 21
-0.61198474312898 22
-0.6974699100544388 23
-0.7280709176620441 24
-0.8049961379233492 25
-0.7612786017898687 26
-0.6781809945300699 27
-0.6026298326962067 28
-0.6348594972144164 29
-0.5337880631531913 30
-0.7255977426492293 31
-0.934099041587364 32
-1.2200240123601662 33
-1.147864914371251 34
-1.135066715030392 35
-1.2721067942577218 36
-1.2781015707742251 37
-1.3067300106421063 38
-1.2416161696843284 39
-1.391612467603264 40
-1.2596775418569757 41
-1.4256234507951207 42
-1.390763770081274 43
-1.3912970121344261 44
-1.6349453185537897 45
-1.716594461667631 46
-1.479544516829876 47
-1.3785205658144886 48
-1.551073867780811 49
-1.5506176850980387 50
-0.8744407032883301 51
-1.008251961117005 52
-0.7902531381658022 53
-0.6758490759646287 54
-0.82257639356966 55
-0.5400694756524584 56
-0.6212688068923788 57
-0.6946875746424783 58
-0.908459406129208 59
-0.9518422608254473 60
-0.8218319590474978 61
-0.7779160444271889 62
-0.8378247481016472 63
-0.7843778996102237 64
-0.7343758201970401 65
-0.7147558752529444 66
-0.6779162072919862 67
-0.671071989135114 68
-0.5299213736001762 69
-0.5725149441065509 70
-0.5586273457323445 71
-0.5635616511445993 72
-1.4244897555526603 73
-0.6806895191280447 74
-0.49000940938256865 75
-1.497026227630176 76
-1.564960685032064 77
-1.5364645209562635 78
-1.5213522149540624 79
-1.4738221616284646 80
-1.5529908935351626 81
-1.493902544719159 82
-1.1881731509471898 83
-0.5017893943959562 84
-0.507472016778586 85
-0.530311025406655 86
-0.984492784519956 87
-1.0540382872950613 88
-1.3116495638575354 89
-1.209816203307854 90
-1.456424392710884 91
-2.4103215863396876 92
-3.42191221321743 93
-3.5900967359931126 94
-3.72076354789489 95
-4.23009000199252 96
-4.6453817009155935 97
-7.443651544225563 98
-6.129267138287477 99
-6.539999510863717 100
-9.744576678932019 101
-7.21566114050427 102
-12.013854055540648 103
-12.847897699458974 104
-13.615564286246444 105
-14.274078137110775 106
-19.86623835699873 107
-19.93521472384528 108
-31.130453600040724 109
-38.118181683541216 110
-85.06339040086624 111
-163.50288705467068 112
-1071.4918569400504 113
-21.75833417276669 114
-9.57841038582858 115
-18.159578763823042 116
-11.954496698519446 117
-17.4045626345819 118
-13.586236481600439 119
-11.070973941923464 120
-9.621552459408294 121
-13.742399965746792 122
-15.921239927562048 123
-15.632819591673375 124
-8.616536171112916 125
-5.4925892328732555 126
-9.111153483049769 127
-4.191810149250979 128
-3.8008959312523283 129
-3.434710984758615 130
-1.958714167091924 131
-1.8116215687798798 132
-2.3028120105570755 133
-0.9322457123703407 134
-1.114182859880008 135
-1.0439759222373657 136
-0.4069621943545241 137
-0.38664304698900964 138
-0.7861031572831256 139
-0.9298481340418989 140
-0.5648647708072265 141
-0.2144021322062568 142
-0.2800700968204075 143
-0.2861339492235089 144
-0.29422321849086 145
-0.10184780344324484 146
-0.48068579739386985 147
-0.5011206106835073 148
-0.5908880330529904 149
In [672]:
mod = ForecasterAutoregDirect(regressor=LinearRegression() ,steps = 7 ,lags=12)
In [673]:
mod.fit(X_train)
In [674]:
X_cv.iloc[:7].plot()
mod.predict().plot()
Out[674]:
<AxesSubplot:xlabel='Order Date2'>

this method isn't good ,i can use non linear model to capture more complex relation

In [681]:
import lightgbm
import xgboost
import catboost
In [682]:
from lightgbm import LGBMRegressor
from xgboost import XGBRegressor
from catboost import CatBoostRegressor
In [677]:
for _ in range(1,20,1):
    mod = ForecasterAutoregDirect(regressor=LGBMRegressor(random_state=0) ,steps = 7 ,lags=_)
    mod.fit(X_train)
    print(r2_score(X_cv.iloc[:7].values,mod.predict().values),_)

-2.605092634271937 1
-0.590586794170475 2
0.30284228012024617 3
-0.17571585634025277 4
-0.14787494078216357 5
-0.3483119058843649 6
-0.3223297303387971 7
-0.0822019061889343 8
0.14212159083446008 9
0.2792531738702769 10
0.03436860402601938 11
-0.14045345717596924 12
-0.3008839671997221 13
-0.2619015186858109 14
0.1841655008048435 15
-0.16333372565882498 16
0.05778642260367772 17
-0.36563928177340443 18
-0.07165292320937078 19
In [680]:
for _ in range(1,20,1):
    mod = ForecasterAutoregDirect(regressor=XGBRegressor(random_state=0) ,steps = 7 ,lags=_)
    mod.fit(X_train)
    print(r2_score(X_cv.iloc[:7].values,mod.predict().values),_)

-4.683889161863324 1
-2.1698883695813134 2
-1.8007475652170366 3
-0.9717920789410659 4
-0.8654299340742153 5
-0.15954545914923535 6
-1.272109457146267 7
0.29386573867806176 8
-0.13066636604543191 9
0.47967224372468653 10
0.5188539870011358 11
-0.32414254039842527 12
-0.25267380382320725 13
-0.5455551602734832 14
-0.1994447674365698 15
-0.9590458671668733 16
-0.7264633107128671 17
-0.7834277585951652 18
-0.07719442304788982 19
In [683]:
for _ in range(1,20,1):
    mod = ForecasterAutoregDirect(regressor=CatBoostRegressor(random_state=0) ,steps = 7 ,lags=_)
    mod.fit(X_train)
    print(r2_score(X_cv.iloc[:7].values,mod.predict().values),_)

-4.176834933936596 1
-1.6600386500110926 2
-0.3566079123118393 3
-0.14008101947779728 4
-0.018524897271272378 5
0.022164404829809303 6
-0.054877116791302916 7
-0.36061452869335486 8
-0.05256705833257702 9
-0.03740795722274326 10
-0.08860242345642244 11
-0.5253673936846301 12
-0.5024780217323075 13
-0.9161020993339637 14
-0.8066366794294093 15
-0.6556217795503541 16
-0.5057646645903662 17
-0.4259435314376747 18
-0.4026432605123147 19
In [686]:
m = X_train.max()
In [691]:
Xt = X_train/m
Xc = X_cv/m
In [692]:
for _ in range(1,150,1):
    mod = ForecasterAutoregDirect(regressor=LinearRegression() ,steps = 7 ,lags=_)
    mod.fit(Xt)
    print(r2_score(Xc.iloc[:7].values,mod.predict().values),_)
    Xc.iloc[:7].plot()
    mod.predict().plot()
    plt.show()

-2.151254054856313 1

-0.42260733390226424 2

-0.13729987215929773 3

-0.3122692947599419 4

-0.25077923176843275 5

-0.25702278775502996 6

-0.15663578838216563 7

-0.14354521125239983 8

-0.08719138136947446 9

-0.06287553021658399 10

-0.007681173672710262 11

-0.40452355662884676 12

-0.37323508785562765 13

-0.4254453393113655 14

-0.42771074908205287 15

-0.5003433551974188 16

-0.5128451704595891 17

-0.42490954033980644 18

-0.3755096542251597 19

-0.45983939711527433 20

-0.4558106509998352 21

-0.6119847431289789 22

-0.6974699100544401 23

-0.7280709176620481 24

-0.804996137923343 25

-0.7612786017898607 26

-0.6781809945300623 27

-0.6026298326962074 28

-0.6348594972144177 29

-0.533788063153189 30

-0.725597742649229 31

-0.9340990415873653 32

-1.2200240123601644 33

-1.147864914371246 34

-1.1350667150303932 35

-1.2721067942577196 36

-1.2781015707742225 37

-1.3067300106421063 38

-1.241616169684331 39

-1.391612467603275 40

-1.259677541856988 41

-1.4256234507951278 42

-1.3907637700812878 43

-1.3912970121344403 44

-1.6349453185537994 45

-1.7165944616676456 46

-1.479544516829888 47

-1.3785205658144957 48

-1.5510738677808202 49

-1.5506176850980684 50

-0.8744407032883419 51

-1.00825196111701 52

-0.7902531381658122 53

-0.6758490759646378 54

-0.8225763935696682 55

-0.5400694756524587 56

-0.621268806892384 57

-0.6946875746424761 58

-0.9084594061292115 59

-0.9518422608254486 60

-0.8218319590475118 61

-0.7779160444271929 62

-0.8378247481016485 63

-0.7843778996102235 64

-0.7343758201970343 65

-0.7147558752529373 66

-0.6779162072919851 67

-0.6710719891351049 68

-0.5299213736001764 69

-0.5725149441065509 70

-0.5586273457323405 71

-0.5635616511446007 72

-1.4244897555526412 73

-0.6806895191280442 74

-0.4900094093825509 75

-1.497026227630153 76

-1.5649606850320588 77

-1.5364645209562457 78

-1.5213522149540522 79

-1.4738221616284481 80

-1.552990893535155 81

-1.4939025447191332 82

-1.1881731509471711 83

-0.501789394395945 84

-0.5074720167785964 85

-0.5303110254066565 86

-0.9844927845199527 87

-1.0540382872950689 88

-1.311649563857519 89

-1.209816203307852 90

-1.4564243927108778 91

-2.4103215863397383 92

-3.4219122132174506 93

-3.59009673599311 94

-3.7207635478949124 95

-4.230090001992562 96

-4.64538170091564 97

-7.443651544225629 98

-6.12926713828753 99

-6.5399995108637246 100

-9.744576678932058 101

-7.215661140504297 102

-12.013854055540698 103

-12.847897699458985 104

-13.61556428624643 105

-14.274078137110697 106

-19.866238356998892 107

-19.93521472384538 108

-31.130453600040674 109

-38.11818168354148 110

-85.06339040086695 111

-163.50288705467136 112

-1071.4918569400488 113

-56.224907466655715 114

-17.37806460730822 115

-21.2332521722473 116

-12.014015792555211 117

-17.074466648449103 118

-13.045427239994964 119

-11.56430658725896 120

-9.761203744537807 121

-13.532873079620645 122

-15.646320878221285 123

-16.43038893623468 124

-8.268514508314116 125

-5.3907856329858514 126

-7.622182310031295 127

-3.7099921572206034 128

-4.453031262198642 129

-3.6203795510962227 130

-1.8130855389095202 131

-1.6498036911663276 132

-2.1322459822202107 133

-0.9943377061300904 134

-0.926722323709837 135

-0.9448711130814862 136

-0.6046240926179218 137

-0.5054381990730934 138

-1.0904821034134673 139

-0.8697724018753259 140

-0.7698029055412476 141

-0.2379946216518356 142

-0.3432292480050749 143

-0.4001308160464865 144

-0.4075688812743181 145

-0.27814692482441794 146

-0.4597536412302001 147

-0.4175512431029247 148

-0.5906353524760526 149
  • i think there is Extrapolated problem ,but it turned out is this method is bad
In [709]:
x=[]
y=[]
for _ in range(len(time_series)-13):
        print(time_series.values[_:_+7],0,time_series.values[_+7:_+14])
        x.append(time_series.values[_:_+7]),y.append(time_series.values[_+7:_+14])
    

[65420.91 70407.84 46793.74 61779.51 46160.62 52500.25 53493.66] 0 [55868.09 54944.27 56114.35 78304.82 47792.25 61021.67 49844.91]
[70407.84 46793.74 61779.51 46160.62 52500.25 53493.66 55868.09] 0 [54944.27 56114.35 78304.82 47792.25 61021.67 49844.91 63988.26]
[46793.74 61779.51 46160.62 52500.25 53493.66 55868.09 54944.27] 0 [56114.35 78304.82 47792.25 61021.67 49844.91 63988.26 51065.39]
[61779.51 46160.62 52500.25 53493.66 55868.09 54944.27 56114.35] 0 [78304.82 47792.25 61021.67 49844.91 63988.26 51065.39 54931.56]
[46160.62 52500.25 53493.66 55868.09 54944.27 56114.35 78304.82] 0 [47792.25 61021.67 49844.91 63988.26 51065.39 54931.56 48291.98]
[52500.25 53493.66 55868.09 54944.27 56114.35 78304.82 47792.25] 0 [61021.67 49844.91 63988.26 51065.39 54931.56 48291.98 56709.41]
[53493.66 55868.09 54944.27 56114.35 78304.82 47792.25 61021.67] 0 [49844.91 63988.26 51065.39 54931.56 48291.98 56709.41 67913.55]
[55868.09 54944.27 56114.35 78304.82 47792.25 61021.67 49844.91] 0 [63988.26 51065.39 54931.56 48291.98 56709.41 67913.55 60655.77]
[54944.27 56114.35 78304.82 47792.25 61021.67 49844.91 63988.26] 0 [51065.39 54931.56 48291.98 56709.41 67913.55 60655.77 59216.85]
[56114.35 78304.82 47792.25 61021.67 49844.91 63988.26 51065.39] 0 [54931.56 48291.98 56709.41 67913.55 60655.77 59216.85 57046.3 ]
[78304.82 47792.25 61021.67 49844.91 63988.26 51065.39 54931.56] 0 [48291.98 56709.41 67913.55 60655.77 59216.85 57046.3  55640.48]
[47792.25 61021.67 49844.91 63988.26 51065.39 54931.56 48291.98] 0 [56709.41 67913.55 60655.77 59216.85 57046.3  55640.48 57405.81]
[61021.67 49844.91 63988.26 51065.39 54931.56 48291.98 56709.41] 0 [67913.55 60655.77 59216.85 57046.3  55640.48 57405.81 71063.06]
[49844.91 63988.26 51065.39 54931.56 48291.98 56709.41 67913.55] 0 [60655.77 59216.85 57046.3  55640.48 57405.81 71063.06 63490.03]
[63988.26 51065.39 54931.56 48291.98 56709.41 67913.55 60655.77] 0 [59216.85 57046.3  55640.48 57405.81 71063.06 63490.03 56816.99]
[51065.39 54931.56 48291.98 56709.41 67913.55 60655.77 59216.85] 0 [57046.3  55640.48 57405.81 71063.06 63490.03 56816.99 61511.91]
[54931.56 48291.98 56709.41 67913.55 60655.77 59216.85 57046.3 ] 0 [55640.48 57405.81 71063.06 63490.03 56816.99 61511.91 56051.97]
[48291.98 56709.41 67913.55 60655.77 59216.85 57046.3  55640.48] 0 [57405.81 71063.06 63490.03 56816.99 61511.91 56051.97 61018.77]
[56709.41 67913.55 60655.77 59216.85 57046.3  55640.48 57405.81] 0 [71063.06 63490.03 56816.99 61511.91 56051.97 61018.77 70921.79]
[67913.55 60655.77 59216.85 57046.3  55640.48 57405.81 71063.06] 0 [63490.03 56816.99 61511.91 56051.97 61018.77 70921.79 77481.89]
[60655.77 59216.85 57046.3  55640.48 57405.81 71063.06 63490.03] 0 [56816.99 61511.91 56051.97 61018.77 70921.79 77481.89 72760.09]
[59216.85 57046.3  55640.48 57405.81 71063.06 63490.03 56816.99] 0 [61511.91 56051.97 61018.77 70921.79 77481.89 72760.09 69913.22]
[57046.3  55640.48 57405.81 71063.06 63490.03 56816.99 61511.91] 0 [56051.97 61018.77 70921.79 77481.89 72760.09 69913.22 75666.94]
[55640.48 57405.81 71063.06 63490.03 56816.99 61511.91 56051.97] 0 [61018.77 70921.79 77481.89 72760.09 69913.22 75666.94 86074.8 ]
[57405.81 71063.06 63490.03 56816.99 61511.91 56051.97 61018.77] 0 [70921.79 77481.89 72760.09 69913.22 75666.94 86074.8  73948.86]
[71063.06 63490.03 56816.99 61511.91 56051.97 61018.77 70921.79] 0 [77481.89 72760.09 69913.22 75666.94 86074.8  73948.86 93573.76]
[63490.03 56816.99 61511.91 56051.97 61018.77 70921.79 77481.89] 0 [72760.09 69913.22 75666.94 86074.8  73948.86 93573.76 87510.93]
[56816.99 61511.91 56051.97 61018.77 70921.79 77481.89 72760.09] 0 [69913.22 75666.94 86074.8  73948.86 93573.76 87510.93 76639.43]
[61511.91 56051.97 61018.77 70921.79 77481.89 72760.09 69913.22] 0 [75666.94 86074.8  73948.86 93573.76 87510.93 76639.43 74835.85]
[56051.97 61018.77 70921.79 77481.89 72760.09 69913.22 75666.94] 0 [86074.8  73948.86 93573.76 87510.93 76639.43 74835.85 80789.87]
[61018.77 70921.79 77481.89 72760.09 69913.22 75666.94 86074.8 ] 0 [73948.86 93573.76 87510.93 76639.43 74835.85 80789.87 92087.09]
[70921.79 77481.89 72760.09 69913.22 75666.94 86074.8  73948.86] 0 [93573.76 87510.93 76639.43 74835.85 80789.87 92087.09 71390.05]
[77481.89 72760.09 69913.22 75666.94 86074.8  73948.86 93573.76] 0 [87510.93 76639.43 74835.85 80789.87 92087.09 71390.05 75294.56]
[72760.09 69913.22 75666.94 86074.8  73948.86 93573.76 87510.93] 0 [76639.43 74835.85 80789.87 92087.09 71390.05 75294.56 87412.53]
[69913.22 75666.94 86074.8  73948.86 93573.76 87510.93 76639.43] 0 [74835.85 80789.87 92087.09 71390.05 75294.56 87412.53 83216.47]
[75666.94 86074.8  73948.86 93573.76 87510.93 76639.43 74835.85] 0 [80789.87 92087.09 71390.05 75294.56 87412.53 83216.47 79050.34]
[86074.8  73948.86 93573.76 87510.93 76639.43 74835.85 80789.87] 0 [92087.09 71390.05 75294.56 87412.53 83216.47 79050.34 75334.78]
[73948.86 93573.76 87510.93 76639.43 74835.85 80789.87 92087.09] 0 [71390.05 75294.56 87412.53 83216.47 79050.34 75334.78 74380.82]
[93573.76 87510.93 76639.43 74835.85 80789.87 92087.09 71390.05] 0 [75294.56 87412.53 83216.47 79050.34 75334.78 74380.82 80589.78]
[87510.93 76639.43 74835.85 80789.87 92087.09 71390.05 75294.56] 0 [87412.53 83216.47 79050.34 75334.78 74380.82 80589.78 81672.08]
[76639.43 74835.85 80789.87 92087.09 71390.05 75294.56 87412.53] 0 [83216.47 79050.34 75334.78 74380.82 80589.78 81672.08 65520.44]
[74835.85 80789.87 92087.09 71390.05 75294.56 87412.53 83216.47] 0 [79050.34 75334.78 74380.82 80589.78 81672.08 65520.44 74414.6 ]
[80789.87 92087.09 71390.05 75294.56 87412.53 83216.47 79050.34] 0 [75334.78 74380.82 80589.78 81672.08 65520.44 74414.6  70799.73]
[92087.09 71390.05 75294.56 87412.53 83216.47 79050.34 75334.78] 0 [74380.82 80589.78 81672.08 65520.44 74414.6  70799.73 79678.29]
[71390.05 75294.56 87412.53 83216.47 79050.34 75334.78 74380.82] 0 [80589.78 81672.08 65520.44 74414.6  70799.73 79678.29 89981.19]
[75294.56 87412.53 83216.47 79050.34 75334.78 74380.82 80589.78] 0 [81672.08 65520.44 74414.6  70799.73 79678.29 89981.19 67944.54]
[87412.53 83216.47 79050.34 75334.78 74380.82 80589.78 81672.08] 0 [65520.44 74414.6  70799.73 79678.29 89981.19 67944.54 90382.17]
[83216.47 79050.34 75334.78 74380.82 80589.78 81672.08 65520.44] 0 [74414.6  70799.73 79678.29 89981.19 67944.54 90382.17 92019.21]
[79050.34 75334.78 74380.82 80589.78 81672.08 65520.44 74414.6 ] 0 [70799.73 79678.29 89981.19 67944.54 90382.17 92019.21 85040.7 ]
[75334.78 74380.82 80589.78 81672.08 65520.44 74414.6  70799.73] 0 [79678.29 89981.19 67944.54 90382.17 92019.21 85040.7  88200.31]
[74380.82 80589.78 81672.08 65520.44 74414.6  70799.73 79678.29] 0 [89981.19 67944.54 90382.17 92019.21 85040.7  88200.31 99455.47]
[80589.78 81672.08 65520.44 74414.6  70799.73 79678.29 89981.19] 0 [67944.54 90382.17 92019.21 85040.7  88200.31 99455.47 85862.34]
[81672.08 65520.44 74414.6  70799.73 79678.29 89981.19 67944.54] 0 [90382.17 92019.21 85040.7  88200.31 99455.47 85862.34 95320.47]
[65520.44 74414.6  70799.73 79678.29 89981.19 67944.54 90382.17] 0 [92019.21 85040.7  88200.31 99455.47 85862.34 95320.47 91492.61]
[74414.6  70799.73 79678.29 89981.19 67944.54 90382.17 92019.21] 0 [85040.7  88200.31 99455.47 85862.34 95320.47 91492.61 93939.66]
[70799.73 79678.29 89981.19 67944.54 90382.17 92019.21 85040.7 ] 0 [88200.31 99455.47 85862.34 95320.47 91492.61 93939.66 82938.04]
[79678.29 89981.19 67944.54 90382.17 92019.21 85040.7  88200.31] 0 [99455.47 85862.34 95320.47 91492.61 93939.66 82938.04 88294.57]
[89981.19 67944.54 90382.17 92019.21 85040.7  88200.31 99455.47] 0 [85862.34 95320.47 91492.61 93939.66 82938.04 88294.57 89825.18]
[67944.54 90382.17 92019.21 85040.7  88200.31 99455.47 85862.34] 0 [95320.47 91492.61 93939.66 82938.04 88294.57 89825.18 87476.48]
[90382.17 92019.21 85040.7  88200.31 99455.47 85862.34 95320.47] 0 [91492.61 93939.66 82938.04 88294.57 89825.18 87476.48 81907.16]
[92019.21 85040.7  88200.31 99455.47 85862.34 95320.47 91492.61] 0 [93939.66 82938.04 88294.57 89825.18 87476.48 81907.16 84080.03]
[85040.7  88200.31 99455.47 85862.34 95320.47 91492.61 93939.66] 0 [82938.04 88294.57 89825.18 87476.48 81907.16 84080.03 80394.07]
[88200.31 99455.47 85862.34 95320.47 91492.61 93939.66 82938.04] 0 [88294.57 89825.18 87476.48 81907.16 84080.03 80394.07 81040.66]
[99455.47 85862.34 95320.47 91492.61 93939.66 82938.04 88294.57] 0 [ 89825.18  87476.48  81907.16  84080.03  80394.07  81040.66 100291.58]
[85862.34 95320.47 91492.61 93939.66 82938.04 88294.57 89825.18] 0 [ 87476.48  81907.16  84080.03  80394.07  81040.66 100291.58  84829.79]
[95320.47 91492.61 93939.66 82938.04 88294.57 89825.18 87476.48] 0 [ 81907.16  84080.03  80394.07  81040.66 100291.58  84829.79  86029.76]
[91492.61 93939.66 82938.04 88294.57 89825.18 87476.48 81907.16] 0 [ 84080.03  80394.07  81040.66 100291.58  84829.79  86029.76  84756.39]
[93939.66 82938.04 88294.57 89825.18 87476.48 81907.16 84080.03] 0 [ 80394.07  81040.66 100291.58  84829.79  86029.76  84756.39 102516.59]
[82938.04 88294.57 89825.18 87476.48 81907.16 84080.03 80394.07] 0 [ 81040.66 100291.58  84829.79  86029.76  84756.39 102516.59  89495.8 ]
[88294.57 89825.18 87476.48 81907.16 84080.03 80394.07 81040.66] 0 [100291.58  84829.79  86029.76  84756.39 102516.59  89495.8   78082.88]
[ 89825.18  87476.48  81907.16  84080.03  80394.07  81040.66 100291.58] 0 [ 84829.79  86029.76  84756.39 102516.59  89495.8   78082.88 101101.3 ]
[ 87476.48  81907.16  84080.03  80394.07  81040.66 100291.58  84829.79] 0 [ 86029.76  84756.39 102516.59  89495.8   78082.88 101101.3   98513.15]
[ 81907.16  84080.03  80394.07  81040.66 100291.58  84829.79  86029.76] 0 [ 84756.39 102516.59  89495.8   78082.88 101101.3   98513.15  94906.21]
[ 84080.03  80394.07  81040.66 100291.58  84829.79  86029.76  84756.39] 0 [102516.59  89495.8   78082.88 101101.3   98513.15  94906.21  91704.17]
[ 80394.07  81040.66 100291.58  84829.79  86029.76  84756.39 102516.59] 0 [ 89495.8   78082.88 101101.3   98513.15  94906.21  91704.17  96563.1 ]
[ 81040.66 100291.58  84829.79  86029.76  84756.39 102516.59  89495.8 ] 0 [ 78082.88 101101.3   98513.15  94906.21  91704.17  96563.1   94199.38]
[100291.58  84829.79  86029.76  84756.39 102516.59  89495.8   78082.88] 0 [101101.3   98513.15  94906.21  91704.17  96563.1   94199.38  90548.6 ]
[ 84829.79  86029.76  84756.39 102516.59  89495.8   78082.88 101101.3 ] 0 [ 98513.15  94906.21  91704.17  96563.1   94199.38  90548.6  112002.59]
[ 86029.76  84756.39 102516.59  89495.8   78082.88 101101.3   98513.15] 0 [ 94906.21  91704.17  96563.1   94199.38  90548.6  112002.59 103580.85]
[ 84756.39 102516.59  89495.8   78082.88 101101.3   98513.15  94906.21] 0 [ 91704.17  96563.1   94199.38  90548.6  112002.59 103580.85 112446.54]
[102516.59  89495.8   78082.88 101101.3   98513.15  94906.21  91704.17] 0 [ 96563.1   94199.38  90548.6  112002.59 103580.85 112446.54 115737.15]
[ 89495.8   78082.88 101101.3   98513.15  94906.21  91704.17  96563.1 ] 0 [ 94199.38  90548.6  112002.59 103580.85 112446.54 115737.15 113545.28]
[ 78082.88 101101.3   98513.15  94906.21  91704.17  96563.1   94199.38] 0 [ 90548.6  112002.59 103580.85 112446.54 115737.15 113545.28 108623.3 ]
[101101.3   98513.15  94906.21  91704.17  96563.1   94199.38  90548.6 ] 0 [112002.59 103580.85 112446.54 115737.15 113545.28 108623.3  103362.4 ]
[ 98513.15  94906.21  91704.17  96563.1   94199.38  90548.6  112002.59] 0 [103580.85 112446.54 115737.15 113545.28 108623.3  103362.4   94219.83]
[ 94906.21  91704.17  96563.1   94199.38  90548.6  112002.59 103580.85] 0 [112446.54 115737.15 113545.28 108623.3  103362.4   94219.83 111820.11]
[ 91704.17  96563.1   94199.38  90548.6  112002.59 103580.85 112446.54] 0 [115737.15 113545.28 108623.3  103362.4   94219.83 111820.11 119968.9 ]
[ 96563.1   94199.38  90548.6  112002.59 103580.85 112446.54 115737.15] 0 [113545.28 108623.3  103362.4   94219.83 111820.11 119968.9  112484.79]
[ 94199.38  90548.6  112002.59 103580.85 112446.54 115737.15 113545.28] 0 [108623.3  103362.4   94219.83 111820.11 119968.9  112484.79 108895.71]
[ 90548.6  112002.59 103580.85 112446.54 115737.15 113545.28 108623.3 ] 0 [103362.4   94219.83 111820.11 119968.9  112484.79 108895.71 105560.71]
[112002.59 103580.85 112446.54 115737.15 113545.28 108623.3  103362.4 ] 0 [ 94219.83 111820.11 119968.9  112484.79 108895.71 105560.71 104712.73]
[103580.85 112446.54 115737.15 113545.28 108623.3  103362.4   94219.83] 0 [111820.11 119968.9  112484.79 108895.71 105560.71 104712.73 109918.72]
[112446.54 115737.15 113545.28 108623.3  103362.4   94219.83 111820.11] 0 [119968.9  112484.79 108895.71 105560.71 104712.73 109918.72 120279.34]
[115737.15 113545.28 108623.3  103362.4   94219.83 111820.11 119968.9 ] 0 [112484.79 108895.71 105560.71 104712.73 109918.72 120279.34 125904.  ]
[113545.28 108623.3  103362.4   94219.83 111820.11 119968.9  112484.79] 0 [108895.71 105560.71 104712.73 109918.72 120279.34 125904.   128361.8 ]
[108623.3  103362.4   94219.83 111820.11 119968.9  112484.79 108895.71] 0 [105560.71 104712.73 109918.72 120279.34 125904.   128361.8  100570.88]
[103362.4   94219.83 111820.11 119968.9  112484.79 108895.71 105560.71] 0 [104712.73 109918.72 120279.34 125904.   128361.8  100570.88  96371.52]
[ 94219.83 111820.11 119968.9  112484.79 108895.71 105560.71 104712.73] 0 [109918.72 120279.34 125904.   128361.8  100570.88  96371.52 119822.84]
[111820.11 119968.9  112484.79 108895.71 105560.71 104712.73 109918.72] 0 [120279.34 125904.   128361.8  100570.88  96371.52 119822.84  88783.21]
[119968.9  112484.79 108895.71 105560.71 104712.73 109918.72 120279.34] 0 [125904.   128361.8  100570.88  96371.52 119822.84  88783.21 111308.25]
[112484.79 108895.71 105560.71 104712.73 109918.72 120279.34 125904.  ] 0 [128361.8  100570.88  96371.52 119822.84  88783.21 111308.25 135791.18]
[108895.71 105560.71 104712.73 109918.72 120279.34 125904.   128361.8 ] 0 [100570.88  96371.52 119822.84  88783.21 111308.25 135791.18 123723.7 ]
[105560.71 104712.73 109918.72 120279.34 125904.   128361.8  100570.88] 0 [ 96371.52 119822.84  88783.21 111308.25 135791.18 123723.7  114603.84]
[104712.73 109918.72 120279.34 125904.   128361.8  100570.88  96371.52] 0 [119822.84  88783.21 111308.25 135791.18 123723.7  114603.84 120092.86]
[109918.72 120279.34 125904.   128361.8  100570.88  96371.52 119822.84] 0 [ 88783.21 111308.25 135791.18 123723.7  114603.84 120092.86 106868.43]
[120279.34 125904.   128361.8  100570.88  96371.52 119822.84  88783.21] 0 [111308.25 135791.18 123723.7  114603.84 120092.86 106868.43 121246.86]
[125904.   128361.8  100570.88  96371.52 119822.84  88783.21 111308.25] 0 [135791.18 123723.7  114603.84 120092.86 106868.43 121246.86 117062.7 ]
[128361.8  100570.88  96371.52 119822.84  88783.21 111308.25 135791.18] 0 [123723.7  114603.84 120092.86 106868.43 121246.86 117062.7  107363.03]
[100570.88  96371.52 119822.84  88783.21 111308.25 135791.18 123723.7 ] 0 [114603.84 120092.86 106868.43 121246.86 117062.7  107363.03  97094.  ]
[ 96371.52 119822.84  88783.21 111308.25 135791.18 123723.7  114603.84] 0 [120092.86 106868.43 121246.86 117062.7  107363.03  97094.   105566.89]
[119822.84  88783.21 111308.25 135791.18 123723.7  114603.84 120092.86] 0 [106868.43 121246.86 117062.7  107363.03  97094.   105566.89 104058.88]
[ 88783.21 111308.25 135791.18 123723.7  114603.84 120092.86 106868.43] 0 [121246.86 117062.7  107363.03  97094.   105566.89 104058.88 109547.09]
[111308.25 135791.18 123723.7  114603.84 120092.86 106868.43 121246.86] 0 [117062.7  107363.03  97094.   105566.89 104058.88 109547.09  95839.12]
[135791.18 123723.7  114603.84 120092.86 106868.43 121246.86 117062.7 ] 0 [107363.03  97094.   105566.89 104058.88 109547.09  95839.12  94469.88]
[123723.7  114603.84 120092.86 106868.43 121246.86 117062.7  107363.03] 0 [ 97094.   105566.89 104058.88 109547.09  95839.12  94469.88 107520.32]
[114603.84 120092.86 106868.43 121246.86 117062.7  107363.03  97094.  ] 0 [105566.89 104058.88 109547.09  95839.12  94469.88 107520.32 108840.35]
[120092.86 106868.43 121246.86 117062.7  107363.03  97094.   105566.89] 0 [104058.88 109547.09  95839.12  94469.88 107520.32 108840.35 106601.24]
[106868.43 121246.86 117062.7  107363.03  97094.   105566.89 104058.88] 0 [109547.09  95839.12  94469.88 107520.32 108840.35 106601.24 108556.59]
[121246.86 117062.7  107363.03  97094.   105566.89 104058.88 109547.09] 0 [ 95839.12  94469.88 107520.32 108840.35 106601.24 108556.59 104357.55]
[117062.7  107363.03  97094.   105566.89 104058.88 109547.09  95839.12] 0 [ 94469.88 107520.32 108840.35 106601.24 108556.59 104357.55  88403.9 ]
[107363.03  97094.   105566.89 104058.88 109547.09  95839.12  94469.88] 0 [107520.32 108840.35 106601.24 108556.59 104357.55  88403.9  102897.39]
[ 97094.   105566.89 104058.88 109547.09  95839.12  94469.88 107520.32] 0 [108840.35 106601.24 108556.59 104357.55  88403.9  102897.39  90430.14]
[105566.89 104058.88 109547.09  95839.12  94469.88 107520.32 108840.35] 0 [106601.24 108556.59 104357.55  88403.9  102897.39  90430.14  97417.87]
[104058.88 109547.09  95839.12  94469.88 107520.32 108840.35 106601.24] 0 [108556.59 104357.55  88403.9  102897.39  90430.14  97417.87 104826.28]
[109547.09  95839.12  94469.88 107520.32 108840.35 106601.24 108556.59] 0 [104357.55  88403.9  102897.39  90430.14  97417.87 104826.28  98594.68]
[ 95839.12  94469.88 107520.32 108840.35 106601.24 108556.59 104357.55] 0 [ 88403.9  102897.39  90430.14  97417.87 104826.28  98594.68 107600.06]
[ 94469.88 107520.32 108840.35 106601.24 108556.59 104357.55  88403.9 ] 0 [102897.39  90430.14  97417.87 104826.28  98594.68 107600.06 100190.49]
[107520.32 108840.35 106601.24 108556.59 104357.55  88403.9  102897.39] 0 [ 90430.14  97417.87 104826.28  98594.68 107600.06 100190.49  99483.46]
[108840.35 106601.24 108556.59 104357.55  88403.9  102897.39  90430.14] 0 [ 97417.87 104826.28  98594.68 107600.06 100190.49  99483.46 111463.25]
[106601.24 108556.59 104357.55  88403.9  102897.39  90430.14  97417.87] 0 [104826.28  98594.68 107600.06 100190.49  99483.46 111463.25  94999.42]
[108556.59 104357.55  88403.9  102897.39  90430.14  97417.87 104826.28] 0 [ 98594.68 107600.06 100190.49  99483.46 111463.25  94999.42 100134.02]
[104357.55  88403.9  102897.39  90430.14  97417.87 104826.28  98594.68] 0 [107600.06 100190.49  99483.46 111463.25  94999.42 100134.02  88183.93]
[ 88403.9  102897.39  90430.14  97417.87 104826.28  98594.68 107600.06] 0 [100190.49  99483.46 111463.25  94999.42 100134.02  88183.93 112927.14]
[102897.39  90430.14  97417.87 104826.28  98594.68 107600.06 100190.49] 0 [ 99483.46 111463.25  94999.42 100134.02  88183.93 112927.14  94530.13]
[ 90430.14  97417.87 104826.28  98594.68 107600.06 100190.49  99483.46] 0 [111463.25  94999.42 100134.02  88183.93 112927.14  94530.13 104178.51]
[ 97417.87 104826.28  98594.68 107600.06 100190.49  99483.46 111463.25] 0 [ 94999.42 100134.02  88183.93 112927.14  94530.13 104178.51 102602.07]
[104826.28  98594.68 107600.06 100190.49  99483.46 111463.25  94999.42] 0 [100134.02  88183.93 112927.14  94530.13 104178.51 102602.07 102472.57]
[ 98594.68 107600.06 100190.49  99483.46 111463.25  94999.42 100134.02] 0 [ 88183.93 112927.14  94530.13 104178.51 102602.07 102472.57  83974.88]
[107600.06 100190.49  99483.46 111463.25  94999.42 100134.02  88183.93] 0 [112927.14  94530.13 104178.51 102602.07 102472.57  83974.88  89857.44]
[100190.49  99483.46 111463.25  94999.42 100134.02  88183.93 112927.14] 0 [ 94530.13 104178.51 102602.07 102472.57  83974.88  89857.44  86578.99]
[ 99483.46 111463.25  94999.42 100134.02  88183.93 112927.14  94530.13] 0 [104178.51 102602.07 102472.57  83974.88  89857.44  86578.99  84330.43]
[111463.25  94999.42 100134.02  88183.93 112927.14  94530.13 104178.51] 0 [102602.07 102472.57  83974.88  89857.44  86578.99  84330.43  86263.85]
[ 94999.42 100134.02  88183.93 112927.14  94530.13 104178.51 102602.07] 0 [102472.57  83974.88  89857.44  86578.99  84330.43  86263.85  87910.42]
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[ 88183.93 112927.14  94530.13 104178.51 102602.07 102472.57  83974.88] 0 [89857.44 86578.99 84330.43 86263.85 87910.42 86937.68 77280.84]
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[ 94530.13 104178.51 102602.07 102472.57  83974.88  89857.44  86578.99] 0 [84330.43 86263.85 87910.42 86937.68 77280.84 89710.33 99047.37]
[104178.51 102602.07 102472.57  83974.88  89857.44  86578.99  84330.43] 0 [86263.85 87910.42 86937.68 77280.84 89710.33 99047.37 71902.78]
[102602.07 102472.57  83974.88  89857.44  86578.99  84330.43  86263.85] 0 [87910.42 86937.68 77280.84 89710.33 99047.37 71902.78 92722.1 ]
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[89857.44 86578.99 84330.43 86263.85 87910.42 86937.68 77280.84] 0 [89710.33 99047.37 71902.78 92722.1  85441.03 81959.64 92288.07]
[86578.99 84330.43 86263.85 87910.42 86937.68 77280.84 89710.33] 0 [99047.37 71902.78 92722.1  85441.03 81959.64 92288.07 90844.13]
[84330.43 86263.85 87910.42 86937.68 77280.84 89710.33 99047.37] 0 [71902.78 92722.1  85441.03 81959.64 92288.07 90844.13 76149.23]
[86263.85 87910.42 86937.68 77280.84 89710.33 99047.37 71902.78] 0 [92722.1  85441.03 81959.64 92288.07 90844.13 76149.23 80709.92]
[87910.42 86937.68 77280.84 89710.33 99047.37 71902.78 92722.1 ] 0 [85441.03 81959.64 92288.07 90844.13 76149.23 80709.92 97624.37]
[86937.68 77280.84 89710.33 99047.37 71902.78 92722.1  85441.03] 0 [81959.64 92288.07 90844.13 76149.23 80709.92 97624.37 93876.87]
[77280.84 89710.33 99047.37 71902.78 92722.1  85441.03 81959.64] 0 [92288.07 90844.13 76149.23 80709.92 97624.37 93876.87 87940.01]
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In [710]:
x
Out[710]:
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 array([136908.24, 159641.83, 154254.87, 146827.73, 151015.66, 152211.89,
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In [711]:
y
Out[711]:
[array([55868.09, 54944.27, 56114.35, 78304.82, 47792.25, 61021.67,
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 array([54944.27, 56114.35, 78304.82, 47792.25, 61021.67, 49844.91,
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 array([63988.26, 51065.39, 54931.56, 48291.98, 56709.41, 67913.55,
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 array([67913.55, 60655.77, 59216.85, 57046.3 , 55640.48, 57405.81,
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 array([60655.77, 59216.85, 57046.3 , 55640.48, 57405.81, 71063.06,
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 array([57046.3 , 55640.48, 57405.81, 71063.06, 63490.03, 56816.99,
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        106087.27]),
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        148318.37]),
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        142006.18]),
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        165977.89]),
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        153811.15]),
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        151185.28]),
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        139109.31]),
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        142497.  ]),
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        146717.44]),
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        162234.28]),
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        145508.87]),
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        143294.24]),
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        152702.  ]),
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        162437.41]),
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        156335.63]),
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        153615.38]),
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        136908.24]),
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        159641.83]),
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        154254.87]),
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        146827.73]),
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        151015.66]),
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        152211.89]),
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        151431.98]),
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        143976.79]),
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        125957.79]),
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        133230.34]),
 array([151015.66, 152211.89, 151431.98, 143976.79, 125957.79, 133230.34,
        154159.42]),
 array([152211.89, 151431.98, 143976.79, 125957.79, 133230.34, 154159.42,
        151493.15]),
 array([151431.98, 143976.79, 125957.79, 133230.34, 154159.42, 151493.15,
        130329.86]),
 array([143976.79, 125957.79, 133230.34, 154159.42, 151493.15, 130329.86,
          8503.4 ])]
In [712]:
x = np.array(x)
In [713]:
y = np.array(y)
In [715]:
y.shape
Out[715]:
(353, 7)
In [716]:
x.shape
Out[716]:
(353, 7)
In [717]:
x = np.expand_dims(x,-1)
In [718]:
_ ,X_test ,_1  ,y_test = train_test_split(x ,y ,shuffle=False)
X_train ,X_cv ,y_train  ,y_cv = train_test_split(_ ,_1 ,shuffle=False)
In [719]:
X_train10 = (X_train)/m
y_train10 = (y_train)/m
X_cv10 = (X_cv)/m
y_cv10 = (y_cv)/m
X_test10 = (X_test)/m
y_test10 = (y_test)/m
In [720]:
import tensorflow
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense ,Bidirectional ,LSTM ,Lambda ,Input ,BatchNormalization
from tensorflow.keras.losses import mean_squared_error
from tensorflow.keras.optimizers import Adam
In [734]:
lstm_model = Sequential([
    Input(X_train.shape[1:]),
    Bidirectional(LSTM(units=7*6,return_sequences=True)),
    Bidirectional(LSTM(units=7*5)),
    Dense(units=7*4, activation='swish'),
    Lambda(lambda x : x+0.01),
    Dense(units=7*3, activation='relu'),
    Dense(units=7*2, activation='relu'),
    Dense(units=7, activation='linear')
])
In [735]:
lstm_model.compile(loss='mse' ,optimizer=Adam(learning_rate=0.0001) ,metrics='mse')
In [736]:
history = lstm_model.fit(x = X_train10 ,y=y_train10 ,validation_data=(X_cv10 ,y_cv10) ,epochs=1000)

Epoch 1/1000
7/7 [==============================] - 38s 859ms/step - loss: 0.4428 - mse: 0.4428 - val_loss: 0.2873 - val_mse: 0.2873
Epoch 2/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.4342 - mse: 0.4342 - val_loss: 0.2815 - val_mse: 0.2815
Epoch 3/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.4265 - mse: 0.4265 - val_loss: 0.2761 - val_mse: 0.2761
Epoch 4/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.4193 - mse: 0.4193 - val_loss: 0.2710 - val_mse: 0.2710
Epoch 5/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.4124 - mse: 0.4124 - val_loss: 0.2659 - val_mse: 0.2659
Epoch 6/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.4052 - mse: 0.4052 - val_loss: 0.2604 - val_mse: 0.2604
Epoch 7/1000
7/7 [==============================] - 0s 23ms/step - loss: 0.3972 - mse: 0.3972 - val_loss: 0.2544 - val_mse: 0.2544
Epoch 8/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.3884 - mse: 0.3884 - val_loss: 0.2477 - val_mse: 0.2477
Epoch 9/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.3786 - mse: 0.3786 - val_loss: 0.2402 - val_mse: 0.2402
Epoch 10/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.3674 - mse: 0.3674 - val_loss: 0.2320 - val_mse: 0.2320
Epoch 11/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.3550 - mse: 0.3550 - val_loss: 0.2227 - val_mse: 0.2227
Epoch 12/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.3410 - mse: 0.3410 - val_loss: 0.2127 - val_mse: 0.2127
Epoch 13/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.3254 - mse: 0.3254 - val_loss: 0.2015 - val_mse: 0.2015
Epoch 14/1000
7/7 [==============================] - 0s 57ms/step - loss: 0.3078 - mse: 0.3078 - val_loss: 0.1893 - val_mse: 0.1893
Epoch 15/1000
7/7 [==============================] - 0s 55ms/step - loss: 0.2886 - mse: 0.2886 - val_loss: 0.1764 - val_mse: 0.1764
Epoch 16/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.2683 - mse: 0.2683 - val_loss: 0.1634 - val_mse: 0.1634
Epoch 17/1000
7/7 [==============================] - 0s 49ms/step - loss: 0.2479 - mse: 0.2479 - val_loss: 0.1512 - val_mse: 0.1512
Epoch 18/1000
7/7 [==============================] - 0s 56ms/step - loss: 0.2285 - mse: 0.2285 - val_loss: 0.1393 - val_mse: 0.1393
Epoch 19/1000
7/7 [==============================] - 0s 56ms/step - loss: 0.2090 - mse: 0.2090 - val_loss: 0.1274 - val_mse: 0.1274
Epoch 20/1000
7/7 [==============================] - 0s 57ms/step - loss: 0.1901 - mse: 0.1901 - val_loss: 0.1162 - val_mse: 0.1162
Epoch 21/1000
7/7 [==============================] - 0s 47ms/step - loss: 0.1720 - mse: 0.1720 - val_loss: 0.1066 - val_mse: 0.1066
Epoch 22/1000
7/7 [==============================] - 0s 57ms/step - loss: 0.1583 - mse: 0.1583 - val_loss: 0.1002 - val_mse: 0.1002
Epoch 23/1000
7/7 [==============================] - 0s 62ms/step - loss: 0.1466 - mse: 0.1466 - val_loss: 0.0932 - val_mse: 0.0932
Epoch 24/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.1359 - mse: 0.1359 - val_loss: 0.0867 - val_mse: 0.0867
Epoch 25/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.1256 - mse: 0.1256 - val_loss: 0.0801 - val_mse: 0.0801
Epoch 26/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.1159 - mse: 0.1159 - val_loss: 0.0740 - val_mse: 0.0740
Epoch 27/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.1064 - mse: 0.1064 - val_loss: 0.0687 - val_mse: 0.0687
Epoch 28/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0980 - mse: 0.0980 - val_loss: 0.0633 - val_mse: 0.0633
Epoch 29/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0895 - mse: 0.0895 - val_loss: 0.0574 - val_mse: 0.0574
Epoch 30/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0814 - mse: 0.0814 - val_loss: 0.0529 - val_mse: 0.0529
Epoch 31/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0757 - mse: 0.0757 - val_loss: 0.0507 - val_mse: 0.0507
Epoch 32/1000
7/7 [==============================] - 0s 45ms/step - loss: 0.0716 - mse: 0.0716 - val_loss: 0.0488 - val_mse: 0.0488
Epoch 33/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0684 - mse: 0.0684 - val_loss: 0.0470 - val_mse: 0.0470
Epoch 34/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0652 - mse: 0.0652 - val_loss: 0.0451 - val_mse: 0.0451
Epoch 35/1000
7/7 [==============================] - 0s 42ms/step - loss: 0.0623 - mse: 0.0623 - val_loss: 0.0432 - val_mse: 0.0432
Epoch 36/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0595 - mse: 0.0595 - val_loss: 0.0413 - val_mse: 0.0413
Epoch 37/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0569 - mse: 0.0569 - val_loss: 0.0394 - val_mse: 0.0394
Epoch 38/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0545 - mse: 0.0545 - val_loss: 0.0382 - val_mse: 0.0382
Epoch 39/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0523 - mse: 0.0523 - val_loss: 0.0368 - val_mse: 0.0368
Epoch 40/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0501 - mse: 0.0501 - val_loss: 0.0355 - val_mse: 0.0355
Epoch 41/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0482 - mse: 0.0482 - val_loss: 0.0344 - val_mse: 0.0344
Epoch 42/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0464 - mse: 0.0464 - val_loss: 0.0334 - val_mse: 0.0334
Epoch 43/1000
7/7 [==============================] - 0s 47ms/step - loss: 0.0446 - mse: 0.0446 - val_loss: 0.0319 - val_mse: 0.0319
Epoch 44/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0429 - mse: 0.0429 - val_loss: 0.0310 - val_mse: 0.0310
Epoch 45/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0413 - mse: 0.0413 - val_loss: 0.0302 - val_mse: 0.0302
Epoch 46/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0398 - mse: 0.0398 - val_loss: 0.0293 - val_mse: 0.0293
Epoch 47/1000
7/7 [==============================] - 0s 46ms/step - loss: 0.0383 - mse: 0.0383 - val_loss: 0.0282 - val_mse: 0.0282
Epoch 48/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0369 - mse: 0.0369 - val_loss: 0.0271 - val_mse: 0.0271
Epoch 49/1000
7/7 [==============================] - 0s 50ms/step - loss: 0.0356 - mse: 0.0356 - val_loss: 0.0265 - val_mse: 0.0265
Epoch 50/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0343 - mse: 0.0343 - val_loss: 0.0259 - val_mse: 0.0259
Epoch 51/1000
7/7 [==============================] - 0s 45ms/step - loss: 0.0331 - mse: 0.0331 - val_loss: 0.0248 - val_mse: 0.0248
Epoch 52/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0320 - mse: 0.0320 - val_loss: 0.0239 - val_mse: 0.0239
Epoch 53/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0309 - mse: 0.0309 - val_loss: 0.0235 - val_mse: 0.0235
Epoch 54/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0298 - mse: 0.0298 - val_loss: 0.0228 - val_mse: 0.0228
Epoch 55/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0288 - mse: 0.0288 - val_loss: 0.0222 - val_mse: 0.0222
Epoch 56/1000
7/7 [==============================] - 0s 49ms/step - loss: 0.0278 - mse: 0.0278 - val_loss: 0.0216 - val_mse: 0.0216
Epoch 57/1000
7/7 [==============================] - 0s 47ms/step - loss: 0.0269 - mse: 0.0269 - val_loss: 0.0212 - val_mse: 0.0212
Epoch 58/1000
7/7 [==============================] - 0s 52ms/step - loss: 0.0260 - mse: 0.0260 - val_loss: 0.0203 - val_mse: 0.0203
Epoch 59/1000
7/7 [==============================] - 0s 48ms/step - loss: 0.0252 - mse: 0.0252 - val_loss: 0.0199 - val_mse: 0.0199
Epoch 60/1000
7/7 [==============================] - 0s 55ms/step - loss: 0.0244 - mse: 0.0244 - val_loss: 0.0195 - val_mse: 0.0195
Epoch 61/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0236 - mse: 0.0236 - val_loss: 0.0189 - val_mse: 0.0189
Epoch 62/1000
7/7 [==============================] - 0s 60ms/step - loss: 0.0229 - mse: 0.0229 - val_loss: 0.0183 - val_mse: 0.0183
Epoch 63/1000
7/7 [==============================] - 0s 61ms/step - loss: 0.0221 - mse: 0.0221 - val_loss: 0.0179 - val_mse: 0.0179
Epoch 64/1000
7/7 [==============================] - 0s 66ms/step - loss: 0.0214 - mse: 0.0214 - val_loss: 0.0177 - val_mse: 0.0177
Epoch 65/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0208 - mse: 0.0208 - val_loss: 0.0171 - val_mse: 0.0171
Epoch 66/1000
7/7 [==============================] - 0s 51ms/step - loss: 0.0201 - mse: 0.0201 - val_loss: 0.0168 - val_mse: 0.0168
Epoch 67/1000
7/7 [==============================] - 0s 53ms/step - loss: 0.0195 - mse: 0.0195 - val_loss: 0.0165 - val_mse: 0.0165
Epoch 68/1000
7/7 [==============================] - 0s 51ms/step - loss: 0.0189 - mse: 0.0189 - val_loss: 0.0159 - val_mse: 0.0159
Epoch 69/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0184 - mse: 0.0184 - val_loss: 0.0158 - val_mse: 0.0158
Epoch 70/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0179 - mse: 0.0179 - val_loss: 0.0152 - val_mse: 0.0152
Epoch 71/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0174 - mse: 0.0174 - val_loss: 0.0148 - val_mse: 0.0148
Epoch 72/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.0169 - mse: 0.0169 - val_loss: 0.0151 - val_mse: 0.0151
Epoch 73/1000
7/7 [==============================] - 0s 52ms/step - loss: 0.0164 - mse: 0.0164 - val_loss: 0.0145 - val_mse: 0.0145
Epoch 74/1000
7/7 [==============================] - 0s 61ms/step - loss: 0.0159 - mse: 0.0159 - val_loss: 0.0139 - val_mse: 0.0139
Epoch 75/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0155 - mse: 0.0155 - val_loss: 0.0141 - val_mse: 0.0141
Epoch 76/1000
7/7 [==============================] - 0s 57ms/step - loss: 0.0151 - mse: 0.0151 - val_loss: 0.0136 - val_mse: 0.0136
Epoch 77/1000
7/7 [==============================] - 0s 57ms/step - loss: 0.0139 - mse: 0.0139 - val_loss: 0.0121 - val_mse: 0.0121
Epoch 78/1000
7/7 [==============================] - 0s 65ms/step - loss: 0.0122 - mse: 0.0122 - val_loss: 0.0112 - val_mse: 0.0112
Epoch 79/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0113 - mse: 0.0113 - val_loss: 0.0111 - val_mse: 0.0111
Epoch 80/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0108 - mse: 0.0108 - val_loss: 0.0107 - val_mse: 0.0107
Epoch 81/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0104 - mse: 0.0104 - val_loss: 0.0106 - val_mse: 0.0106
Epoch 82/1000
7/7 [==============================] - 0s 69ms/step - loss: 0.0099 - mse: 0.0099 - val_loss: 0.0102 - val_mse: 0.0102
Epoch 83/1000
7/7 [==============================] - 0s 67ms/step - loss: 0.0096 - mse: 0.0096 - val_loss: 0.0100 - val_mse: 0.0100
Epoch 84/1000
7/7 [==============================] - 0s 58ms/step - loss: 0.0093 - mse: 0.0093 - val_loss: 0.0096 - val_mse: 0.0096
Epoch 85/1000
7/7 [==============================] - 0s 59ms/step - loss: 0.0090 - mse: 0.0090 - val_loss: 0.0100 - val_mse: 0.0100
Epoch 86/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0087 - mse: 0.0087 - val_loss: 0.0095 - val_mse: 0.0095
Epoch 87/1000
7/7 [==============================] - 0s 48ms/step - loss: 0.0085 - mse: 0.0085 - val_loss: 0.0094 - val_mse: 0.0094
Epoch 88/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.0082 - mse: 0.0082 - val_loss: 0.0095 - val_mse: 0.0095
Epoch 89/1000
7/7 [==============================] - 0s 56ms/step - loss: 0.0080 - mse: 0.0080 - val_loss: 0.0093 - val_mse: 0.0093
Epoch 90/1000
7/7 [==============================] - 0s 52ms/step - loss: 0.0078 - mse: 0.0078 - val_loss: 0.0093 - val_mse: 0.0093
Epoch 91/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0077 - mse: 0.0077 - val_loss: 0.0090 - val_mse: 0.0090
Epoch 92/1000
7/7 [==============================] - 0s 55ms/step - loss: 0.0076 - mse: 0.0076 - val_loss: 0.0087 - val_mse: 0.0087
Epoch 93/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0074 - mse: 0.0074 - val_loss: 0.0088 - val_mse: 0.0088
Epoch 94/1000
7/7 [==============================] - 0s 47ms/step - loss: 0.0073 - mse: 0.0073 - val_loss: 0.0085 - val_mse: 0.0085
Epoch 95/1000
7/7 [==============================] - 0s 58ms/step - loss: 0.0072 - mse: 0.0072 - val_loss: 0.0086 - val_mse: 0.0086
Epoch 96/1000
7/7 [==============================] - 0s 66ms/step - loss: 0.0070 - mse: 0.0070 - val_loss: 0.0089 - val_mse: 0.0089
Epoch 97/1000
7/7 [==============================] - 0s 66ms/step - loss: 0.0069 - mse: 0.0069 - val_loss: 0.0084 - val_mse: 0.0084
Epoch 98/1000
7/7 [==============================] - 0s 62ms/step - loss: 0.0069 - mse: 0.0069 - val_loss: 0.0084 - val_mse: 0.0084
Epoch 99/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0067 - mse: 0.0067 - val_loss: 0.0088 - val_mse: 0.0088
Epoch 100/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.0066 - mse: 0.0066 - val_loss: 0.0083 - val_mse: 0.0083
Epoch 101/1000
7/7 [==============================] - 0s 62ms/step - loss: 0.0065 - mse: 0.0065 - val_loss: 0.0082 - val_mse: 0.0082
Epoch 102/1000
7/7 [==============================] - 0s 45ms/step - loss: 0.0065 - mse: 0.0065 - val_loss: 0.0085 - val_mse: 0.0085
Epoch 103/1000
7/7 [==============================] - 0s 44ms/step - loss: 0.0064 - mse: 0.0064 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 104/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0063 - mse: 0.0063 - val_loss: 0.0083 - val_mse: 0.0083
Epoch 105/1000
7/7 [==============================] - 0s 52ms/step - loss: 0.0062 - mse: 0.0062 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 106/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0062 - mse: 0.0062 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 107/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0061 - mse: 0.0061 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 108/1000
7/7 [==============================] - 0s 52ms/step - loss: 0.0061 - mse: 0.0061 - val_loss: 0.0082 - val_mse: 0.0082
Epoch 109/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0061 - mse: 0.0061 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 110/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0060 - mse: 0.0060 - val_loss: 0.0083 - val_mse: 0.0083
Epoch 111/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0060 - mse: 0.0060 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 112/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0059 - mse: 0.0059 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 113/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.0059 - mse: 0.0059 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 114/1000
7/7 [==============================] - 0s 46ms/step - loss: 0.0058 - mse: 0.0058 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 115/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0058 - mse: 0.0058 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 116/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0058 - mse: 0.0058 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 117/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0057 - mse: 0.0057 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 118/1000
7/7 [==============================] - 0s 56ms/step - loss: 0.0057 - mse: 0.0057 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 119/1000
7/7 [==============================] - 0s 60ms/step - loss: 0.0057 - mse: 0.0057 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 120/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0057 - mse: 0.0057 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 121/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 122/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 123/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 124/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 125/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 126/1000
7/7 [==============================] - 0s 23ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 127/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 128/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.0057 - mse: 0.0057 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 129/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 130/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0082 - val_mse: 0.0082
Epoch 131/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 132/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 133/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 134/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 135/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 136/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 137/1000
7/7 [==============================] - 0s 24ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 138/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 139/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 140/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 141/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 142/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 143/1000
7/7 [==============================] - 0s 46ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 144/1000
7/7 [==============================] - 0s 48ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 145/1000
7/7 [==============================] - 0s 58ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 146/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 147/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 148/1000
7/7 [==============================] - 0s 49ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0082 - val_mse: 0.0082
Epoch 149/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 150/1000
7/7 [==============================] - 0s 46ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 151/1000
7/7 [==============================] - 0s 60ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 152/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 153/1000
7/7 [==============================] - 0s 66ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 154/1000
7/7 [==============================] - 0s 53ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 155/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 156/1000
7/7 [==============================] - 0s 45ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 157/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 158/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 159/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 160/1000
7/7 [==============================] - 0s 42ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 161/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 162/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 163/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 164/1000
7/7 [==============================] - 0s 46ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 165/1000
7/7 [==============================] - 0s 51ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 166/1000
7/7 [==============================] - 0s 63ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 167/1000
7/7 [==============================] - 0s 42ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 168/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 169/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 170/1000
7/7 [==============================] - 0s 53ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 171/1000
7/7 [==============================] - 0s 48ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 172/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 173/1000
7/7 [==============================] - 0s 63ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 174/1000
7/7 [==============================] - 0s 64ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 175/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 176/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 177/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 178/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 179/1000
7/7 [==============================] - 0s 58ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 180/1000
7/7 [==============================] - 0s 59ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 181/1000
7/7 [==============================] - 0s 62ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 182/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 183/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 184/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 185/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 186/1000
7/7 [==============================] - 0s 58ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 187/1000
7/7 [==============================] - 0s 50ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 188/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 189/1000
7/7 [==============================] - 0s 52ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 190/1000
7/7 [==============================] - 0s 58ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 191/1000
7/7 [==============================] - 0s 57ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 192/1000
7/7 [==============================] - 0s 53ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 193/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 194/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 195/1000
7/7 [==============================] - 0s 50ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 196/1000
7/7 [==============================] - 0s 46ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 197/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 198/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 199/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 200/1000
7/7 [==============================] - 0s 51ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 201/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 202/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 203/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 204/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 205/1000
7/7 [==============================] - 0s 50ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 206/1000
7/7 [==============================] - 0s 56ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 207/1000
7/7 [==============================] - 0s 58ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 208/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 209/1000
7/7 [==============================] - 0s 46ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 210/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 211/1000
7/7 [==============================] - 0s 66ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 212/1000
7/7 [==============================] - 0s 48ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 213/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 214/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 215/1000
7/7 [==============================] - 0s 42ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 216/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 217/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 218/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 219/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 220/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 221/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 222/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 223/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 224/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 225/1000
7/7 [==============================] - 0s 26ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 226/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 227/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 228/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 229/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 230/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 231/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 232/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 233/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 234/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 235/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 236/1000
7/7 [==============================] - 0s 44ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 237/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 238/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 239/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 240/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 241/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 242/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 243/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 244/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 245/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 246/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 247/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 248/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 249/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 250/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 251/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 252/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 253/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 254/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 255/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 256/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 257/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 258/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 259/1000
7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 260/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 261/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 262/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 263/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 264/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 265/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 266/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 267/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 268/1000
7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 269/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 270/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 271/1000
7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 272/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 273/1000
7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 274/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 275/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 276/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 277/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 278/1000
7/7 [==============================] - 0s 23ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 279/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 280/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 281/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 282/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 283/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 284/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 285/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 286/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 287/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 288/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 289/1000
7/7 [==============================] - 0s 21ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 290/1000
7/7 [==============================] - 0s 26ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 291/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 292/1000
7/7 [==============================] - 0s 22ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 293/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 294/1000
7/7 [==============================] - 0s 20ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 295/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 296/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 297/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 298/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 299/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 300/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0082 - val_mse: 0.0082
Epoch 301/1000
7/7 [==============================] - 0s 24ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 302/1000
7/7 [==============================] - 0s 25ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0083 - val_mse: 0.0083
Epoch 303/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 304/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 305/1000
7/7 [==============================] - 0s 24ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 306/1000
7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 307/1000
7/7 [==============================] - 0s 22ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 308/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 309/1000
7/7 [==============================] - 0s 24ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 310/1000
7/7 [==============================] - 0s 26ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 311/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 312/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 313/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 314/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 315/1000
7/7 [==============================] - 0s 24ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 316/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 317/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 318/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 319/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 320/1000
7/7 [==============================] - 0s 44ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 321/1000
7/7 [==============================] - 0s 47ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 322/1000
7/7 [==============================] - 0s 48ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 323/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 324/1000
7/7 [==============================] - 0s 49ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 325/1000
7/7 [==============================] - 0s 47ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 326/1000
7/7 [==============================] - 0s 45ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 327/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 328/1000
7/7 [==============================] - 0s 56ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 329/1000
7/7 [==============================] - 0s 53ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 330/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 331/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 332/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 333/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 334/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 335/1000
7/7 [==============================] - 0s 51ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 336/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 337/1000
7/7 [==============================] - 0s 48ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 338/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 339/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 340/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 341/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 342/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 343/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 344/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 345/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 346/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 347/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 348/1000
7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 349/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 350/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 351/1000
7/7 [==============================] - 0s 48ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 352/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 353/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 354/1000
7/7 [==============================] - 0s 61ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 355/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 356/1000
7/7 [==============================] - 0s 55ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 357/1000
7/7 [==============================] - 0s 52ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 358/1000
7/7 [==============================] - 0s 56ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 359/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 360/1000
7/7 [==============================] - 0s 52ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 361/1000
7/7 [==============================] - 0s 50ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 362/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 363/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 364/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 365/1000
7/7 [==============================] - 0s 59ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 366/1000
7/7 [==============================] - 0s 53ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 367/1000
7/7 [==============================] - 0s 47ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 368/1000
7/7 [==============================] - 0s 61ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 369/1000
7/7 [==============================] - 0s 58ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 370/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 371/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 372/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 373/1000
7/7 [==============================] - 0s 56ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 374/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 375/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 376/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 377/1000
7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 378/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 379/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 380/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 381/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 382/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 383/1000
7/7 [==============================] - 0s 59ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 384/1000
7/7 [==============================] - 0s 59ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 385/1000
7/7 [==============================] - 0s 47ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 386/1000
7/7 [==============================] - 0s 24ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 387/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 388/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 389/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 390/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 391/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 392/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 393/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 394/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 395/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 396/1000
7/7 [==============================] - 0s 61ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 397/1000
7/7 [==============================] - 0s 60ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 398/1000
7/7 [==============================] - 0s 57ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 399/1000
7/7 [==============================] - 0s 64ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 400/1000
7/7 [==============================] - 0s 66ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 401/1000
7/7 [==============================] - 0s 65ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 402/1000
7/7 [==============================] - 0s 46ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 403/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 404/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 405/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 406/1000
7/7 [==============================] - 0s 45ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 407/1000
7/7 [==============================] - 0s 50ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 408/1000
7/7 [==============================] - 0s 65ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 409/1000
7/7 [==============================] - 0s 66ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 410/1000
7/7 [==============================] - 0s 68ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 411/1000
7/7 [==============================] - 0s 46ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 412/1000
7/7 [==============================] - 0s 48ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 413/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 414/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 415/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 416/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 417/1000
7/7 [==============================] - 0s 61ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 418/1000
7/7 [==============================] - 0s 47ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 419/1000
7/7 [==============================] - 0s 46ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 420/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 421/1000
7/7 [==============================] - 0s 47ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 422/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 423/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 424/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 425/1000
7/7 [==============================] - 0s 56ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 426/1000
7/7 [==============================] - 0s 55ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 427/1000
7/7 [==============================] - 0s 57ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 428/1000
7/7 [==============================] - 0s 65ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 429/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 430/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 431/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 432/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 433/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 434/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 435/1000
7/7 [==============================] - 0s 47ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 436/1000
7/7 [==============================] - 0s 49ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 437/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 438/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 439/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 440/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 441/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 442/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 443/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 444/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 445/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 446/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 447/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 448/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 449/1000
7/7 [==============================] - 0s 45ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 450/1000
7/7 [==============================] - 0s 45ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 451/1000
7/7 [==============================] - 0s 61ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 452/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 453/1000
7/7 [==============================] - 0s 42ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 454/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 455/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 456/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 457/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 458/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 459/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 460/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 461/1000
7/7 [==============================] - 0s 46ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 462/1000
7/7 [==============================] - 0s 58ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 463/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 464/1000
7/7 [==============================] - 0s 48ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 465/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 466/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 467/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 468/1000
7/7 [==============================] - 0s 22ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 469/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 470/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 471/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 472/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 473/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 474/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 475/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 476/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 477/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 478/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 479/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 480/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 481/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 482/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 483/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 484/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 485/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 486/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 487/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 488/1000
7/7 [==============================] - 0s 59ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 489/1000
7/7 [==============================] - 0s 55ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 490/1000
7/7 [==============================] - 0s 51ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 491/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 492/1000
7/7 [==============================] - 0s 49ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 493/1000
7/7 [==============================] - 0s 57ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 494/1000
7/7 [==============================] - 0s 42ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 495/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 496/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 497/1000
7/7 [==============================] - 0s 55ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 498/1000
7/7 [==============================] - 0s 45ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 499/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 500/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 501/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 502/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 503/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 504/1000
7/7 [==============================] - 0s 42ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 505/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 506/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 507/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 508/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 509/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 510/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 511/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 512/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 513/1000
7/7 [==============================] - 0s 26ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 514/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 515/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 516/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 517/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 518/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 519/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 520/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 521/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 522/1000
7/7 [==============================] - 0s 50ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 523/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 524/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 525/1000
7/7 [==============================] - 0s 56ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 526/1000
7/7 [==============================] - 0s 42ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 527/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 528/1000
7/7 [==============================] - 0s 42ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 529/1000
7/7 [==============================] - 0s 49ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 530/1000
7/7 [==============================] - 0s 51ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 531/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 532/1000
7/7 [==============================] - 0s 62ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 533/1000
7/7 [==============================] - 0s 51ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 534/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 535/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 536/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 537/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 538/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 539/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 540/1000
7/7 [==============================] - 0s 23ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 541/1000
7/7 [==============================] - 0s 42ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 542/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 543/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 544/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 545/1000
7/7 [==============================] - 0s 52ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 546/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 547/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 548/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 549/1000
7/7 [==============================] - 0s 24ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 550/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 551/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 552/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 553/1000
7/7 [==============================] - 0s 42ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 554/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 555/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 556/1000
7/7 [==============================] - 0s 50ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 557/1000
7/7 [==============================] - 0s 46ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 558/1000
7/7 [==============================] - 0s 53ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 559/1000
7/7 [==============================] - 0s 49ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 560/1000
7/7 [==============================] - 0s 45ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 561/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 562/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 563/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 564/1000
7/7 [==============================] - 0s 59ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 565/1000
7/7 [==============================] - 0s 49ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 566/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 567/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 568/1000
7/7 [==============================] - 0s 44ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 569/1000
7/7 [==============================] - 0s 44ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 570/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 571/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 572/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 573/1000
7/7 [==============================] - 0s 51ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 574/1000
7/7 [==============================] - 0s 60ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 575/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 576/1000
7/7 [==============================] - 0s 26ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 577/1000
7/7 [==============================] - 0s 26ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 578/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 579/1000
7/7 [==============================] - 0s 26ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 580/1000
7/7 [==============================] - 0s 23ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 581/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 582/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 583/1000
7/7 [==============================] - 0s 56ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 584/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 585/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 586/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 587/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 588/1000
7/7 [==============================] - 0s 44ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 589/1000
7/7 [==============================] - 0s 42ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 590/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 591/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 592/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 593/1000
7/7 [==============================] - 0s 24ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 594/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 595/1000
7/7 [==============================] - 0s 49ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 596/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 597/1000
7/7 [==============================] - 0s 26ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 598/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 599/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 600/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 601/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 602/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 603/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 604/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 605/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 606/1000
7/7 [==============================] - 0s 22ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 607/1000
7/7 [==============================] - 0s 21ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 608/1000
7/7 [==============================] - 0s 20ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 609/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 610/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 611/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 612/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 613/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 614/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 615/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 616/1000
7/7 [==============================] - 0s 26ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 617/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 618/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 619/1000
7/7 [==============================] - 0s 20ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 620/1000
7/7 [==============================] - 0s 19ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 621/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 622/1000
7/7 [==============================] - 0s 20ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 623/1000
7/7 [==============================] - 0s 26ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 624/1000
7/7 [==============================] - 0s 21ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 625/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 626/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0082 - val_mse: 0.0082
Epoch 627/1000
7/7 [==============================] - 0s 24ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 628/1000
7/7 [==============================] - 0s 19ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 629/1000
7/7 [==============================] - 0s 26ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 630/1000
7/7 [==============================] - 0s 20ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 631/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 632/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 633/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 634/1000
7/7 [==============================] - 0s 21ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 635/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 636/1000
7/7 [==============================] - 0s 57ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 637/1000
7/7 [==============================] - 0s 45ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 638/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 639/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 640/1000
7/7 [==============================] - 0s 50ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 641/1000
7/7 [==============================] - 0s 22ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 642/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 643/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 644/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 645/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 646/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 647/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 648/1000
7/7 [==============================] - 0s 19ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 649/1000
7/7 [==============================] - 0s 19ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 650/1000
7/7 [==============================] - 0s 19ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 651/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 652/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 653/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 654/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 655/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 656/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 657/1000
7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 658/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 659/1000
7/7 [==============================] - 0s 57ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 660/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 661/1000
7/7 [==============================] - 0s 53ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 662/1000
7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 663/1000
7/7 [==============================] - 0s 18ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 664/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 665/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 666/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 667/1000
7/7 [==============================] - 0s 25ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 668/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 669/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 670/1000
7/7 [==============================] - 0s 19ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 671/1000
7/7 [==============================] - 0s 22ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 672/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 673/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 674/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 675/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 676/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 677/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 678/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 679/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 680/1000
7/7 [==============================] - 0s 25ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 681/1000
7/7 [==============================] - 0s 19ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 682/1000
7/7 [==============================] - 0s 24ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 683/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 684/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 685/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 686/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 687/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 688/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 689/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 690/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0057 - mse: 0.0057 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 691/1000
7/7 [==============================] - 0s 21ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 692/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 693/1000
7/7 [==============================] - 0s 21ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 694/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 695/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 696/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 697/1000
7/7 [==============================] - 0s 24ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 698/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 699/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 700/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 701/1000
7/7 [==============================] - 0s 22ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 702/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 703/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 704/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 705/1000
7/7 [==============================] - 0s 22ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 706/1000
7/7 [==============================] - 0s 19ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 707/1000
7/7 [==============================] - 0s 21ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 708/1000
7/7 [==============================] - 0s 23ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 709/1000
7/7 [==============================] - 0s 21ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 710/1000
7/7 [==============================] - 0s 23ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 711/1000
7/7 [==============================] - 0s 24ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 712/1000
7/7 [==============================] - 0s 21ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 713/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 714/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 715/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 716/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 717/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 718/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 719/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 720/1000
7/7 [==============================] - 0s 22ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 721/1000
7/7 [==============================] - 0s 22ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 722/1000
7/7 [==============================] - 0s 25ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 723/1000
7/7 [==============================] - 0s 18ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 724/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 725/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 726/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 727/1000
7/7 [==============================] - 0s 26ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 728/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 729/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 730/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 731/1000
7/7 [==============================] - 0s 47ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 732/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 733/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 734/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 735/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 736/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 737/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 738/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 739/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 740/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 741/1000
7/7 [==============================] - 0s 23ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 742/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 743/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 744/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 745/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 746/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 747/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 748/1000
7/7 [==============================] - 0s 23ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 749/1000
7/7 [==============================] - 0s 19ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 750/1000
7/7 [==============================] - 0s 23ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 751/1000
7/7 [==============================] - 0s 22ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 752/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 753/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 754/1000
7/7 [==============================] - 0s 21ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 755/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 756/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 757/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 758/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 759/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 760/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 761/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 762/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 763/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 764/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 765/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 766/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 767/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 768/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 769/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 770/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 771/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 772/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 773/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 774/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 775/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 776/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 777/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 778/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 779/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 780/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 781/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 782/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 783/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 784/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 785/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 786/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 787/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 788/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 789/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 790/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 791/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 792/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 793/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 794/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 795/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 796/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 797/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 798/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 799/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 800/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 801/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 802/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 803/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 804/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 805/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 806/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 807/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 808/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 809/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 810/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 811/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 812/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 813/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 814/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 815/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 816/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 817/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 818/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 819/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 820/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 821/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 822/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 823/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 824/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 825/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 826/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 827/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 828/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 829/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 830/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 831/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 832/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 833/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 834/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 835/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 836/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 837/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 838/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 839/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 840/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 841/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 842/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 843/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 844/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 845/1000
7/7 [==============================] - 0s 45ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 846/1000
7/7 [==============================] - 0s 48ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 847/1000
7/7 [==============================] - 0s 42ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 848/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0072 - val_mse: 0.0072
Epoch 849/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 850/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 851/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0072 - val_mse: 0.0072
Epoch 852/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 853/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 854/1000
7/7 [==============================] - 0s 47ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 855/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 856/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 857/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 858/1000
7/7 [==============================] - 0s 40ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 859/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 860/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 861/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 862/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 863/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 864/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 865/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 866/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 867/1000
7/7 [==============================] - 0s 44ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 868/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 869/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 870/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 871/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 872/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 873/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 874/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 875/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 876/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 877/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 878/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 879/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 880/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 881/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 882/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 883/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 884/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 885/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 886/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 887/1000
7/7 [==============================] - 0s 47ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 888/1000
7/7 [==============================] - 0s 45ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 889/1000
7/7 [==============================] - 0s 51ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 890/1000
7/7 [==============================] - 0s 48ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 891/1000
7/7 [==============================] - 0s 26ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 892/1000
7/7 [==============================] - 0s 56ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 893/1000
7/7 [==============================] - 0s 50ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 894/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 895/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 896/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 897/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 898/1000
7/7 [==============================] - 0s 53ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 899/1000
7/7 [==============================] - 0s 53ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 900/1000
7/7 [==============================] - 0s 42ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 901/1000
7/7 [==============================] - 0s 42ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 902/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 903/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 904/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 905/1000
7/7 [==============================] - 0s 50ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 906/1000
7/7 [==============================] - 0s 59ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 907/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 908/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 909/1000
7/7 [==============================] - 0s 46ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 910/1000
7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 911/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 912/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 913/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 914/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 915/1000
7/7 [==============================] - 0s 53ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 916/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 917/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 918/1000
7/7 [==============================] - 0s 52ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 919/1000
7/7 [==============================] - 0s 52ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 920/1000
7/7 [==============================] - 0s 47ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 921/1000
7/7 [==============================] - 0s 26ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 922/1000
7/7 [==============================] - 0s 58ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 923/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 924/1000
7/7 [==============================] - 0s 56ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 925/1000
7/7 [==============================] - 0s 55ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 926/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 927/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 928/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 929/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 930/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 931/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 932/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 933/1000
7/7 [==============================] - 0s 51ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 934/1000
7/7 [==============================] - 0s 45ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 935/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 936/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 937/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 938/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 939/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 940/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 941/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 942/1000
7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 943/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 944/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 945/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 946/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 947/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 948/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 949/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 950/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 951/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 952/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 953/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 954/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 955/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 956/1000
7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 957/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 958/1000
7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 959/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 960/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 961/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 962/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 963/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 964/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 965/1000
7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 966/1000
7/7 [==============================] - 0s 32ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 967/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 968/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 969/1000
7/7 [==============================] - 0s 23ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 970/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 971/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 972/1000
7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 973/1000
7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079
Epoch 974/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 975/1000
7/7 [==============================] - 0s 47ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 976/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 977/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 978/1000
7/7 [==============================] - 0s 46ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 979/1000
7/7 [==============================] - 0s 47ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 980/1000
7/7 [==============================] - 0s 42ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 981/1000
7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075
Epoch 982/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 983/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 984/1000
7/7 [==============================] - 0s 52ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 985/1000
7/7 [==============================] - 0s 41ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 986/1000
7/7 [==============================] - 0s 25ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 987/1000
7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 988/1000
7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077
Epoch 989/1000
7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 990/1000
7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 991/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0057 - mse: 0.0057 - val_loss: 0.0078 - val_mse: 0.0078
Epoch 992/1000
7/7 [==============================] - 0s 26ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0072 - val_mse: 0.0072
Epoch 993/1000
7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0081 - val_mse: 0.0081
Epoch 994/1000
7/7 [==============================] - 0s 22ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0072 - val_mse: 0.0072
Epoch 995/1000
7/7 [==============================] - 0s 26ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0074 - val_mse: 0.0074
Epoch 996/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080
Epoch 997/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073
Epoch 998/1000
7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 999/1000
7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
Epoch 1000/1000
7/7 [==============================] - 0s 22ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
In [737]:
plt.subplots(figsize=(14,8))
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
sns.despine()
plt.show()
In [738]:
trainin_predi = (lstm_model.predict(X_train10))
cv_predi = (lstm_model.predict(X_cv10))
test_predi = (lstm_model.predict(X_test10))

7/7 [==============================] - 3s 6ms/step
3/3 [==============================] - 0s 11ms/step
3/3 [==============================] - 0s 7ms/step
In [739]:
plt.subplots(figsize=(14,8))
plt.plot(y_train10.flatten()*m)
plt.plot(trainin_predi.flatten()*m)
sns.despine()
plt.show()
In [740]:
plt.subplots(figsize=(14,8))
plt.plot(y_cv10.flatten()*m)
plt.plot(cv_predi.flatten()*m)
sns.despine()
plt.show()
In [741]:
plt.subplots(figsize=(14,8))
plt.plot(y_test10.flatten()*m)
plt.plot(test_predi.flatten()*m)
sns.despine()
plt.show()
In [771]:
from scipy.stats import t
sample_mean = np.mean((lstm_model.predict(y_test[-1].reshape(1,7,1))*m).flatten())
sample_std = np.std((lstm_model.predict(y_test[-1].reshape(1,7,1))*m).flatten(), ddof=1)  # ddof=1 for sample standard deviation

# Desired confidence level (e.g., 95%)
confidence_level = 0.95

# Degrees of freedom for a time series is typically sample size - 1
degrees_of_freedom = 7 - 1

# Calculate the t-score
t_score = t.ppf((1 + confidence_level) / 2, degrees_of_freedom)

# Calculate the margin of error
margin_of_error = t_score * (sample_std / np.sqrt(7))

# Confidence interval
lower_bound = sample_mean - margin_of_error
upper_bound = sample_mean + margin_of_error

1/1 [==============================] - 0s 40ms/step
1/1 [==============================] - 0s 30ms/step
In [776]:
plt.subplots(figsize=(14,8))
plt.plot(time_series[-7:].values)
plt.plot(np.append(time_series[-7:].values,(lstm_model.predict(y_test[-1].reshape(1,7,1))*m).flatten()),'--')
plt.plot(np.append(time_series[-7:].values,np.array([lower_bound]*7)),'--')
plt.plot(np.append(time_series[-7:].values,np.array([upper_bound]*7)),'--')
sns.despine()
plt.show()

1/1 [==============================] - 0s 24ms/step